2024 - Journal publications
Sofie Lövdal and Michael Biehl
Iterated Relevance Matrix Analysis (IRMA) for the
identification of class-discriminative subspaces
Neurocomputing 577: Art. No. 127367 (2024)
R. van Veen, N. R. Bari Tamboli, S Lövdal et al.
Supspace corrected relevance learning with application in neuroimaging
Artificial Intelligence in Medicine 149: Art. No. 102786 (2024)
H. Miller, D. Harman, G. Padur Aithal, P. Manousou, J.F. Cobbold, R. Parker,
D. Sheridan, P.N. Newsome, F. Karpe, M. Neville, W. Arlt, A.J. Sitch,
M. Korbonits, M Biehl, W. Alazawi, J.W. Tomlinson
Translating the potential of the urine steroid metabolome to stage NAFLD (TrUSt-NAFLD): study protocol for a multicentre, prospective validation study,
BMJ Open 14(1): Art. no. e074918 (2024)
A. Prete,K. Lang, D. Pavlov, et al.
Urine
steroid metabolomics as a diagnostic tool in primary aldosteronism
J. Steroid Biochemistry and Molecular Biology, Art. 106445, 2024.
2023 - Journal publications
Htet Htet Htun, M. Biehl, N. Petkov
Survey
of feature selection and extraction techniques for stock market prediction
Financial Innovation 9: article number 26 (open access, 2023)
doi: 10.1186/s40854-022-00441-7
2023 - Conference contributions
S. Lövdal, M. Biehl
Improved Interpretation of Feature Relevances:
Iterated Relevance Matrix Analysis (IRMA)
In: M. Verleysen, Proc. ESANN 2023,
European Symposium on Artificial Neural Networks, Computational Intelligence
and Machine Learning
Ciaco-i6doc.com, 2023 (available online)
F. Richert, M. Straat, E. Oostwal, M. Biehl
Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis
In: M. Verleysen, Proc. ESANN 2023,
European Symposium on Artificial Neural Networks, Computational Intelligence
and Machine Learning
Ciaco-i6doc.com, 2023 (available online)
2023 - Book chapters
E.L. van den Brandhof, A.M.M. van der Stouwe, J.R. Dalenberg, I. Tuitert, M.A.J. Tijssen, M. Biehl
Machine learning basic concepts for the movements disorders specialist
In: A. Sanchez Ferro, M.H.G. Monje (eds.), International Review of
Movement Disorders - Digital Technologies in Movement Disorders, Vol. 5, pages
21-47, Elsevier, 2023
2023 - Monographs and edited volumes
M. Biehl
The Shallow and the Deep: A biased introduction
to neural networks and old school machine learning
open access and print on demand, 290 pages, University of Groningen Press
(2023)
R. Smedinga, M. Biehl (editors)
SC@RUG 2022-2023,
Proc. of the 20th
Student Colloquium Computer Science
University of Groningen, 109 pages, 2023 (open access)
2022 - Journal publications
S. Rezaei, J.P. McKean, M. Biehl, W. de Roo, A. Lafontaine
A machine learning based approach to gravitational lens identification with the International LOFAR Telescope
Monthly Notices of the Royal Astronomical Society
517 (1): 1156-1170 (open access, 2022)
doi: 10.1093/mnras/stac2078
S. Ghosh, E. Baranowski, M. Biehl, W. Arlt, P. Tino, K. Bunte
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets
arXiv preprint arXiv:2206.02056 (2022)
}
R. van Veen, S.K. Meles, R.J. Renken, F.E. Reesink, W.H. Oertel, A. Janzenl,
G.-J. de Vries, K.L. Leenders, M. Biehl
FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder
Computer Methods and Programs in Biomedicine 225: 107042 (2022)
doi: 10.1016/j.cmpb.2022.107042
S. Rezaei, J. McKean, M. Biehl, A. Javadpour
DECORAS: detection and characterization of radio-astonomical sources using deep learning
Monthly Notices of the Royal Astronomical Society 510(4): 5891-5907 (2022)
doi: 10.1093/mnras/stab3519
2022 - Conference contributions
T. Villmann, D. Staps, D., J. Ravichandran, S. Saralajew, M. Biehl, M. Kaden
A Learning Vector Quantization architecture for transfer
learning based classification
in case of multiple sources
by means of null-space evaluation
In: T. Bouadi, E. Fromont, E. Hüllermeier (eds.).
Advances in Intelligent Data Analysis XX. IDA 2022
Lecture Notes in Computer Science, vol 13205. Springer, Cham (2022)
doi: 10.1007/978-3-031-01333-1_28
2022 - Monographs, edited volumes
M. Biehl
The Shallow and the Deep: A biased introduction to neural networks
and old school machine learning
Lecture Notes (freely available pre-publication version),
University of Groningen, 288 pages (2022)
Final version (2023, open access and print on demand)
R. Smedinga, M. Biehl (editors)
SC@RUG 2021-2022
Proc. of the 19th
Student Colloquium Computer Science
University of Groningen, 127 pages, 2022 (open access)
2022 - Technical Reports, abstracts, other publications
E.L. v.d. Brandhof, S. v.d. Veen, G. Russo, I. Tuitert, J. Dalenberg,
M. v.d. Stouwe, J. Elting, M. Biehl, M. de Koning-Tijssen
Towards a Machine Learning Based Classification System for Hyperkinetic Movement Disorders:
Generating a Neurophysiological Feature Set.
Mov Disord. 2022; 37 (suppl 1).
A. Prete, L. Abdi, M. Canducci, A.E. Taylor, L.C. Gilligan, A. Albors-Zumel, E. van den Brandhof, Y. Zhang, K.N. Manolopoulos, P. Tino, M. Biehl, W. Dunn, W. Arlt
Combining steroid and global metabolome profiling by mass spectrometry with machine learning to investigate metabolic risk in benign adrenal tumours with mild autonomous cortisol secretion
Endocrine Abstracts 83: AO2 (2022)
doi: 10.1530/endoabs.83.AO2
A. Taylor, I. Bancos, L. Gilligan, R. van Veen, V. Chortis, F. Shaheen,
C. Jenkinson, D.M. O'Neil, B. Hughes, J.M. Hawley, B. Keevil, C.H.L.
Shackelton, J. Deeks, A.J. Sitch, M. Biehl, W. Arlt
Urinary steroid metabolomics for adrenocortical cancer diagnosis. Comparison of gas chromatography mass spectrometry to liquid chromatography mass spectrometry
Endocrine Abstracts 81: P386 (2022)
doi: 10.1530/endoabs.81.P386
2021 - Journal publications
R. van Veen, M. Biehl, G.-J. de Vries
sklvq: Scikit Learning Vector Quantization
Journal of Machine Learning Research 22(231): 1-6, 2021.
G. Owomugisha, F. Melchert, E. Mwebaze, J.A. Quinn, M. Biehl
Matrix
Relevance Learning From Spectral Data for Diagnosing Cassava Diseases
IEEE Access, Volume 9, pp. 83355-83363, 2021
M. Straat, F. Abadi, Z. Kan, C. Göpfert, B. Hammer, M. Biehl
Supervised learning in the presence of concept drift: a modelling framework
Neural Computing and Applications (2021, open access)
preprint: arXiv 2005.10531
Elisa Oostwal, Michiel Straat, Michael Biehl
Hidden Unit Specialization: ReLU vs. sigmoidal activation
Physica A 564:125517, 2021 (open access, available online: Nov. 2020)
2021 - Conference Contributions
M. Münch, M. Straat, M. Biehl, F.-M. Schleif
Complex-Valued Embedding of Generic Proximity Data
In: S+SSPR 2021, LNCS Vol. 12644: 14-23 (2021)
2021 - Technical Reports, abstracts, other publications
V. Chortis, A.J. Sitch, I. Bancos, A. Prete, A.E. Taylor, M. Biehl, J.J. Deeks, W. Arlt
Comment on A Modern Assessment of Cancer Risk in Adrenal Incidentalomas
Annals of Surgery: online publication ahead of print (2021)
2020 - Journal publications
R. van Veen, V. Gurvits, R.V. Kogan, S.K. Meles, G.-J. de Vries, R.J. Renken,
M.C. Rodriguez-Oroz, R. Rodriguez-Rojas, D. Arnaldi, S. Raffa, B.M. de Jong,
K.L. Leenders, M. Biehl
An application of Generalized Matrix Learning
Vector Quantization in Neuroimaging
Computer Methods and Programs in Biomedicine, Vol. 197: 105708, 2020
(open access)
doi: 10.1016/j.cmpb.2020.105708
I. Bancos, A. Taylor, V. Chortis, A. Sitch, et al.
A. Panda, A. Yadav, H. Yeerna, A. Singh, M. Biehl,
M. Lux, A. Schulz,
A. Moolla, J. de Boer, D. Pavlov, A. Amin et al.
L. Pfannschmidt, J. Jakob, F. Hindler, M. Biehl, P. Tino, B. Hammer
2020 - Conference publications
G. Owomugisha, P.K.B. Mugagga, F. Melchert, E. Mwebaze, J.A. Quinn, M. Biehl
G. Owomugisha, E. Nuwamanya, J.A. Quinn, M. Biehl,
E. Mwebaze
M. Münch, C. Raab, M. Biehl, F.-M. Schleif
V. Chortis, I. Bancos, T. Nijman, L.C. Gilligan, A.E. Taylor et al.
Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas
in the EURINE-ACT study: a prospective test validation study
The Lancet Diabetes and Endocrinology, 2020 (open access)
doi: 10.1016/S2213-8587(20)30218-7
T. Klecha, S. Doniach, H. Khiabanian, S. Ganesan, P. Tamayo, G. Bhanot
Tissue-
and development-stage-specific mRNA and heterogeneous CNV signatures
of human ribosomal proteins in normal and cancer sample
Nucleic Acids Research 48(13): 7079-7098, 2020 (open access)
doi: 10.1093/nar/gkaa485
Accurate non-invasive diagnosis and staging of non-alcoholic fatty liver disease using the urinary steroid metabolome
Alimentary Pharmacology and Therapeutics 51: 1188-1197, 2020 (open access)
doi: 10.1111/apt.15710
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
Neurocomputing 416: 266-279, 2020 (open access)
doi: 10.1016/j.neucom.2019.12.133
A low-cost 3-D printed
smartphone add-on spectrometer for diagnosis of crop diseases in field
detection of plant diseases using spectral data
In: COMPASS '20 - Proc. 3rd ACM SIGCAS Conf. on Computing and Sustainable Societies,
June 2020, 331-332, 2020 (open access)
doi.org/10.1145/3378393.3402252
Early
detection of plant diseases using spectral data
In: APPIS 2020 - Proc. 3rd Intl. Conf. on Applications
of Intelligent Systems, Art. No. 26, 6 pages, 2020
doi: 10.1145/3378184.3378222
Structure
Preserving Encoding of Non-Euclidean Similarity Data
In: ICPRAM - Proc. 9th Intl. Conf. on Pattern Recognition,
Applications and Methods, 2020 (open access)
doi: 10.5220/0008955100430051
Publications in 2019
2019 - Journal publications
Urine steroid metabolomics as a novel tool for detection of recurrent adrenocortical carcinoma
J. Clinical Endocrinology and Metabolism,
105(3): e307-e318, 2019 (open access)
doi: 10.1210/clinem/dgz141
F. Melchert, G. Bani, U. Seiffert, M. Biehl
Adaptive basis functions for prototype-based classification of
functional data
Neural Computing and Applications, 32: 18213-18223 (2020) (online open access: 2019)
A Nolte, L. Wang, M. Bilicki, B. Holwerda, B., M. Biehl
Galaxy
classification: A machine learning analysis of GAMA catalogue data
Neurocomputing 342: 172-190, 2019 (open access)
doi: 10.1016/j.neucom.2018.12.076
M. Straat, M. Kaden, M. Gay, T. Villmann
A. Lampe, U. Seiffert, M. Biehl, F. Melchert
Learning vector quantization and relevances in complex coefficient space
Neural Computing and Applications: 18085-18099, 2020 (online open access 2019)
2019 - Conference publications
M. Biehl, N. Caticha, M. Opper, T. Villmann
Statistical physics of learning and inference
In: M. Verleysen, Proc. ESANN 27,
European Symposium on Artificial Neural Networks
Ciaco-i6doc.com, 501-506, 2019 (open access)
M. Straat, M. Biehl
On-line
learning dynamics of ReLU neural networks using statistical physics techniques
In: M. Verleysen, Proc. ESANN 27,
European Symposium on Artificial Neural Networks
Ciaco-i6doc.com, 517-522, 2019 (open access)
L. Pfannschmidt, J. Jakob, M. Biehl, P. Tino, B. Hammer
Feature
Relevance bounds for ordinal regression
In: M. Verleysen, Proc. ESANN 27,
European Symposium on Artificial Neural Networks
Ciaco-i6doc.com, 343-348, 2019 (open access)
M. Biehl, F. Abadi, C. Göpfert, B. Hammer
Prototype-based
classifiers in the presence of concept drift:
A modelling framework (arXiv:1903.07273)
in: Proc. WSOM+
13th Workshop on Self-Organizing
Maps and Learning Vector Quantization, Springer, 2019
A.C. Costa, B. Barufaldi, L.R. Borges, M. Biehl,
A.D.A. Maidment, M.A.C. Vieira
Analysis of feature relevance using an image quality index applied to digital mammography
In: SPIE 10948, Proc. Medical Imaging 2019: Physics of Medical Imaging, 109485R (March 2019);
doi: 10.1117/12.2512975
D.L. Idema, Y. Wang, M. Biehl, P.L. Horvatovich, E. Hak
Effect estimate comparison between the prescription sequence symmetry analysis (PSSA) and parallel group study designs: A systematic review
PLoS ONE 13(12): e0208389 (2018, open access)
M. Straat, F. Abadi, C. Göpfert, B. Hammer, M. Biehl
Statistical Mechanics of On-Line Learning Under Concept Drift
Entropy 20(10): Art. 775 (2018)
open access, doi: 10.3390/e20100775
T. Villmann, M. Kaden, W. Herrmann, M. Biehl
Learning
Vector Quantization classifiers for ROC-optimization
Computational Statistics 33(3): 1173-1194 (2018)
available online (2016), doi: 10.1007/s00180-016-0678-y
2018 - Conference publications
R. van Veen, L. Talavera Martinez, R.V. Kogan, S.K. Meles,
D. Mudali, J.B.T.M. Roerdink, F. Massa, M. Grazzini, J.A. Obeso,
M.C. Rodriguez-Oroz, K.L. Leenders, R.J. Renken, J.J.G. de Vries,
M. Biehl
Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases
Preprint version of an article in:
APPIS 2018,
Application of Intelligent Systems.
N. Petkov, N. Strisciuglio, C. Travieso-Gonzalez (eds.),
IOS Press, Frontiers in Artificial Intelligence and Applications, Vol 310: 280-289, 2018
G. Owomugisha, F. Melchert, E. Mwebaze, J.A. Quinn, M. Biehl
Machine Learning for diagnosis of disease in plants using spectral data
In: ICAI'18, Proc. of the Intl. Conf. Artificial Intelligence 2018,
CSREA Press, pp. 9-15, 2018 (open access)
A. Nolte, L. Wang, M. Biehl
Prototype-based analysis of GAMA galaxy catalogue data
In: ESANN 2018,, M. Verleysen (editor),
Proc. of the 26th European Symposium on Artificial
Neural Networks 2018 (Bruges, Belgium),
Ciaco-i6doc.com, 339-344, 2018 (open access)
M. Biehl, K. Bunte, G. Longo, P. Tino
Machine Learning and Data Analysis in Astroinformatics
In: ESANN 2018, M. Verleysen (editor),
Proc. of the 26th European Symposium on Artificial
Neural Networks 2018 (Bruges, Belgium),
Ciaco-i6doc.com, 307-314, 2018 (open access)
2018 - Edited volumes, special issues
F. Aiolli, M. Biehl, L. Onoto
Advances
in artificial neural networks, machine learning
and computational intelligence
(link to the journal page through science direct),
Editorial of a special issue,
Neurocomputing 298: 1-3, 2018
10.1016/j.neucom.2018.01.090
R. Smedinga, M. Biehl (editors)
SC@RUG 2017-2018,
Proc. of the 15th
Student Colloquium Computer Science
University of Groningen, 116 pages, 2018 (open access)
2018 - Technical Reports, abstracts, other publications
T. Villmann, J.R.D. Ravichandran, S. Saralajew, M. Biehl
Dropout in Learning Vector Quantization
Networks for Regularized Learning and
Classification Confidence Estimation
Machine Learning Reports
MLR-01-2018, pg. 15-21, 2018
M. Biehl
The statistical physics of learning in a nutshell (abstract)
Machine Learning Reports
MLR-01-2018, p. 23, 2018
K. Taxis, J. de Boer, H.G. van der Meer, M. Biehl
Reducing the drug burden index - A post hoc analysis of a randomised
controlled trial using machine learning (abstract)
Pharmacoepidemiology and Drug Safety 27: 504, 2018
doi: 10.1002/pds.4629
A. Moolla, A. Taylor, L. Gilligan, J. De Boer, D. Pavlov, B. Hughes, Z. Hassan-Smith, M. Armstrong, P. Newsome, T. Shah, L. van Gaal, A. Verrijken, S. Francque, J. Grove, N. Guha, G. Aithal, E. Barnes, W. Arlt, M. Biehl, J. Tomlinson
Staging
of non-alcoholic fatty liver disease through LC-MS/MS analysis of the urinary steroid metabolome
Endocrine Abstracts 59: OC3.3 (2018)
doi:10.1530/endoabs.59.OC3.3
I. Bancos, A. Taylor, V. Chortis, A. Sitch, K. Lang, A. Prete, M. Terzolo, M. Fassnacht, M. Quinkler, D. Kastelan, D. Vassiliadi, F. Beuschlein, U. Ambroziak, M. Biehl, J. Deeks, W. Arlt
Urine
steroid metabolomics as a diagnostic tool for detection of adrenocortical
malignancy - a prospective test validation study
Endocrine Abstracts 56: OC72 (2018)
doi: 10.1530/endoabs.56.OC7.2
W. Arlt, K. Lang, A. Sitch, A.S. Dietz, Y. Rhayem, I. Bancos,
A. Feuchtlinger, V. Chortis, L.C. Gilligan, P. Ludwig, A. Riester,
E. Asbach, B. Hughes, D.M. O'Neill, M. Bidlingmaier, J. Tomlinson,
Z. Hassan-Smith, A. Rees, C. Adolf, S. Hahner, M. Quinkler, T. Dekkers,
J. Deinum, M. Biehl, B. Keevil, C. Shackleton, J.J. Deeks, A.K. Walch,
F. Beuschlein, M. Reincke
Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism
J. of Clinical Investigation, JCI Insight 2(8): 14 pages
(2017, online open access)
2017 - Conference contributions and book chapters
M. Straat, M. Kaden, M. Gay,
T. Villmann, A. Lampe, U. Seiffert, M. Biehl, F. Melchert.
Prototypes
and Matrix Relevance Learning in Complex Fourier Space.
In: WSOM+ 2017, Proc. of the 12th Intl.Workshop on
Self-Organizing Maps, Learning Vector Quantization, Clustering and Visualization,
Nancy/France, IEEE Xplore, 6 pages, 2017.
doi: 10.1109/WSOM.2017.8020019
M. LeKander, M. Biehl, H. de Vries.
Empirical
Evaluation of Gradient Methods
for Matrix Learning Vector Quantization.
In: WSOM+ 2017, Proc. of the 12th Intl.Workshop on
Self-Organizing Maps, Learning Vector Quantization, Clustering and Visualization, Nancy/France, IEEE Xplore, 8 pages, 2017
G. Bani, U. Seiffert, M. Biehl, F. Melchert
Adaptive
Basis Functions for Prototype-based Classification of Functional Data.
In: WSOM+ 2017,Proc. of the 12th Intl. Workshop on Self-Organizing Maps, Learning Vector
Quantization, Clustering and Visualization, Nancy/France,
IEEE Xplore, 8 pages, 2017
T. Villmann, M. Biehl, A. Villmann, S. Saralajew.
Fusion
of Deep Learning Architectures, Multilayer Feedforward Networks
and Learning Vector Quantizers for Deep Classification Learning.
In: WSOM+ 2017, Proc. of the 12th Intl. Workshop on Self-Organizing Maps, Learning Vector
Quantization, Clustering and Visualization, Nancy/France,
IEEE Xplore, 8 pages, 2017
M. Biehl
Biomedical Applications of Prototype Based Classifiers and
Relevance Learning
In: Intl. Conference on Algorithms for Computational Biology AlCoB 2017.
D. Figueiredo, C. Martin-Vide, D. Pratas, M.A. Vega-Rodriguez (eds.)
Sringer LNCS 10252, pp. 3-23, 2017
doi: 10.1007/978-3-319-58163-7
M. Mohammadi, M. Biehl, A. Villmann, T. Villmann
Sequence Learning in Unsupervised and Supervised
Vector Quantization Using Hankel Matrices
In: ICAISC 2017, Proc. Intl. Conf. on Artificial Intelligence and
Soft Computing,
L. Rutkowski et al. (eds.), Springer LNCS 10245, pp 131-142, 2017
Doi: 10.1007/978-3-319-59063-9
A. Neocleous, C. Neocleous, C.N. Schizas, M. Biehl, N. Petkov
G. Bhanot, M. Biehl, T. Vilmmann, D. Zühlke
S. Ghosh, E.S. Baranowski, R. van Veen, G.-J. de Vries, M. Biehl,
W. Arlt, P. Tino, K. Bunte
M. Biehl, B. Hammer, T. Villmann
2017 - Edited volumes, special issues
R. Smedinga, M. Biehl, F. Krameer (editors)
2017 - Technical reports, abstracts, other publications
M. Biehl, F. Abadi, C. Göpfert, B. Hammer
S. Ghosh, E.S. Baranowski, R. van Veen, G.-J. de Vries, M. Biehl,
W. Arlt, P. Tino, K. Bunte
D. Mudali, M. Biehl, S.K. Meles, R.J. Renken, D. Garcia-Garcia,
P. Clavero, J. Arbizu, J.A. Obeso, M.C. Rodriguez-Oroz, K. Leenders, J.B.T.M. Roerdink
M. Biehl, B. Hammer, T. Villmann
F.-M. Schleif, B. Hammer, J.G. Monroy, J.G. Jimenez, J.-L. Blanco-Claraco,
M. Biehl, N. Petkov
2016 - Conference contributions and book chapters
M. Biehl, D. Mudali, K.L. Leenders, J.B.T.M. Roerdink
F. Melchert, U. Seiffert, M. Biehl
F. Melchert, A. Matros, M. Biehl, U. Seiffert
G. Murkherjee, G. Bhanot, K. Raines, S. Sastry, S. Doniach, M. Biehl
G.-J. de Vries, P. Lemmens, D. Brokken, S. Pauws, M. Biehl
E. Mwebaze and M. Biehl
F. Melchert, U. Seiffert and M. Biehl
D. Mudali, M. Biehl, K.L. Leenders and J.B.T.M. Roerdink
M. Gay, M. Kaden, M. Biehl, T. Villmann and A. Lampe
F. Melchert, M. Biehl and U. Seiffert
2016 - Edited volumes, special issues
R. Smedinga, M. Biehl, F. Kramer (editors)
2016 - Technical reports, abstracts, other publications
F. Melchert, U. Seiffert, M. Biehl.
A. Moolla, A. Amin, B. Hughes, W. Arlt, Z. Hassan-Smith, M. Armstrong,
P. Newsome, T. Shah, L. Van Gaal, A. Verrijken, S. Francque, M. Biehl,
J. Tomlinson.
E. Baranowski, K. Bunte, C. Shackleton, A. Taylor, B. Hughes, M. Biehl, P. Tino, T. Guran, W. Arlt
V. Chortis, I. Bancos, A.J. Sitch, A.E. Taylor, D. O'Neil, K. Lang,
M. Quinkler, M. Terzolo, M. Mannelli, D. Vassiliadi, U. Ambroziak,
M. Conall Dennedy, M. Sherlock, J. Bertherat, F. Beuschlein, M. Fassnacht,
J.J. Deeks, M. Biehl, W. Arlt.
A. Moolla, A. Amin, B. Hughes, W. Arlt, Z. Hassan-Smith, M. Armstrong,
P. Newsome, T. Shah, L. Van Gaal, A. Verrijken, S. Francque, M. Biehl,
J. Tomlinson.
O. de Wiljes, R.A. van Elburg, M. Biehl, F.A. Keijzer
Y. Leo, N. Adlard, M. Biehl, M. Juarez, T. Smallie, M. Snow, C.D. Buckley, K. Raza, A. Filer, D. Scheel-Toellner
I. Giotis, N. Molders, S. Land, M. Biehl, M.F. Jonkman, N. Petkov
J.J.G. de Vries, P.M.C. Lemmens, D. Brokken, S.C. Pauws, and M. Biehl
J.J.G. de Vries, S.C. Pauws, and M. Biehl
M. Lange, M. Biehl, T. Villmann
2015 - Conference contributions and book chapters
A. Schulz, B. Mokbel, M. Biehl, B. Hammer
F. Melchert, U. Seiffert, M. Biehl
G.-J. de Vries, S. Pauws M. Biehl
T. Villmann, M. Kaden, D. Nebel and M. Biehl
M. Biehl, B. Hammer, F.-M. Schleif, P. Schneider, T. Villmann
E. Mwebaze, G. Bearda, M. Biehl, D. Zühlke
2015 - Edited volumes, special issues
M. Biehl, A. Ghio, F.-M. Schleif
R. Smedinga, M. Biehl, F. Kramer (editors)
2015 - Technical reports, abstracts, other publications
A. Taylor et al.
K. Lang et al.
V. Chortis et al.
C.F. Davies et al. (link
to list of authors and bibliographical information),
E. Bilal, T. Sakellropoulos, Challenge Participants(*), I.N. Melas,
D.E. Messinis, V. Belcastro, K. Rhrissorrakrai, P. Meyer,
A. Dayarian, R. Romero, Z. Wang, M. Biehl, E. Bilal, S. Hormoz, P. Meyer, R. Norel, K. Rhrissorrakrai, G. Bhanot, F. Luo, A.L. Tarca
S. Hormoz, G. Bhanot, M. Biehl, E. Bilal, P. Meyer, R. Norel, K. Rhrissorrakrai, A. Dayarian
M. Biehl, P. Sadowski, G. Bhanot, E. Bilal, A. Dayarian, P. Meyer, R. Norel, K. Rhrissorrakrai, M.D. Zeller, S. Hormoz
2014 - Conference contributions and book chapters
H.T. Kruitbosch, I. Giotis, M. Biehl
B. Frenay, D. Hofmann, A. Schulz, M. Biehl, B. Hammer
M. Biehl, B. Hammer, T. Villmann
2014 - Edited volumes, special issues
R. Smedinga, M. Biehl, F. Kramer (editors)
2014 - Technical reports, abstracts, other publications
M. Biehl, M. Kaden, T. Villmann
M. Biehl
M. Biehl, M. Kaden, P. Stürmer, T. Villmann.
A. Schulz, D. Hofmann, M. Biehl, B. Hammer
I. Giotis, K. Bunte, N. Petkov, M. Biehl
E. Alegre, M. Biehl, N. Petkov, L. Sanchez
M. Biehl, K. Bunte, P. Schneider
N. Aghaeepour, G. Finak, The FlowCAP Consortium, The DREAM Consortium (*), H. Hoos, T.R. Mosmann, R. Brinkman, R. Gottardo, and R.H. Scheuermann.
2013 - Conference contributions and book chapters
M. Strickert, B. Hammer, T. Villmann, M. Biehl
M. Lange, M. Biehl, T. Villmann
M. Biehl, M. Kästner, M. Lange, T. Villmann
M. Biehl
M.B. Huber, K. Bunte, M.B. Nagarajan, M. Biehl, L.A. Ray, A. Wismüller
M. Kästner, B. Hammer, M. Biehl, T. Villmann
K. Bunte, S. Haase, M. Biehl, T. Villmann
K. Bunte, P. Schneider, B. Hammer, F.-M. Schleif, T. Villmann, M. Biehl
K. Bunte, M. Biehl, B. Hammer
2012 - Conference contributions and book chapters
M. Kästner, D. Nebel, M. Riedel, M. Biehl, T. Villmann
M. Biehl, K. Bunte, F.-M. Schleif, P. Schneider, T. Villmann
G. Peters, K. Bunte, M. Strickert, M. Biehl, T. Villmann
B. Mokbel, W. Lueks, A. Gisbrecht, M. Biehl, B. Hammer
K. Bunte, F.-M. Schleif, M. Biehl
M. Biehl, P. Schneider, D. Smith, H. Stiekema, A. Taylor, B. Hughes, C. Shackleton, P. Stewart, W. Arlt
2012 - Edited volumes, special issues
R. Smedinga, M. Biehl, F. Kramer (editors)
W. Arlt, M. Biehl, A.E. Taylor, S. Hahner, R. Libe, B.A. Hughes, P. Schneider, D.J. Smith, H. Stiekema,
N. Krone,
K. Bunte, M. Biehl, M.F. Jonkman, N. Petkov
K. Bunte, B. Hammer, T. Villmann, M. Biehl, A. Wismüller
E. Mwebaze, P. Schneider, F.-M. Schleif, J.R. Aduwo, J.A. Quinn, S. Haase, T. Villmann, M. Biehl
2011 - Conference proceedings and book chapters
K. Bunte, I. Giotis, N. Petkov, M. Biehl
B. Hammer, M. Biehl, K. Bunte, B. Mokbel
J. Quinn, J. Mooij, T. Heskes, M. Biehl
P. Schneider, T. Geweniger, F.-M. Schleif, M. Biehl, T. Villmann
E. Mwebaze, J. Quinn, M. Biehl
M. Kästner, B. Hammer, M. Biehl, T. Villmann
K. Bunte, M. Biehl, B. Hammer
K. Bunte, M. Biehl, B. Hammer
M.B. Huber, K. Bunte, M.B. Nagajaran, M. Biehl, L.A. Ray, A. Wismüller
2011 - Edited volumes, special issues
R. Smedinga, M. Biehl, F. Kramer (editors)
2011 - Other Publications, Abstracts, Technical Reports, etc.
G. Papari, K. Bunte, M. Biehl
W. Lueks, B. Mokbel, M. Biehl, B. Hammer
M. Kästner, T. Villmann, M. Biehl
A. Witoelar, A. Ghosh, J.J.G. de Vries, B. Hammer, M. Biehl
A. Offringa, G. de Bruyn, M. Biehl, S. Zaroubi, G. Bernardi, V. Pandey
P. Schneider, K. Bunte, H. Stiekema, B. Hammer, T. Villmann, M. Biehl
K. Bunte, B. Hammer, A. Wismüller, M. Biehl
P. Schneider, M. Biehl, B. Hammer
2010 - Conference proceedings and book chapters
A. Offringa, A.G. de Bruyn, S. Zaroubi, M. Biehl
F.-M. Schleif, T. Villmann, B. Hammer, P. Schneider, M. Biehl
E. Mwebaze, P. Schneider, F.-M. Schleif, S. Haase, T. Villmann, M. Biehl
K. Bunte, B. Hammer, T. Villmann, M. Biehl, A. Wismüller
T. Villmann, S. Haase, F.-M. Schleif, B. Hammer, M. Biehl
2010 - Edited volumes, special issues
R. Smedinga, M. Biehl, F. Kramer (editors)
2010 - Other Publications, Abstracts, Technical Reports, etc.
B. Hammer, K. Bunte, M. Biehl
O. de Wijles, R.A.J. van Elburg, M. Biehl, F. Keijzer
A. Taylor, M. Biehl, B. Hughes, H. Stiekema, P. Schneider, D. Smith,
P. Nightingale, C. Shackleton, P. Stewart, W. Arlt
Publications in 2009
2009 - Journal Articles
P. Schneider, M. Biehl, B. Hammer,
P. Schneider, M. Biehl, B. Hammer
A. Witoelar, M. Biehl
2009 - Conference proceedings and book chapters
M. Biehl, B. Hammer, P. Schneider, T. Villmann
T. Villmann, B. Hammer, M. Biehl
M. Biehl, N. Caticha, P. Riegler
K. Bunte, B. Hammer, M. Biehl
M. Strickert, J. Keilwagen, F.-M. Schleif, T. Villmann, M. Biehl
P. Schneider, M. Biehl, B. Hammer
A. Witoelar, M. Biehl, B. Hammer
K. Bunte, M. Biehl, N. Petkov, M.F. Jonkman
K. Bunte, B. Hammer, P. Schneider, M. Biehl
2009 - Edited volumes and issues
M. Biehl, B. Hammer, M. Verleysen, T. Villmann (eds.) F.-M. Schleif, M. Biehl, A. Vellido,
M. Biehl, B. Hammer, S. Hochreiter, S.C. Kremer, T. Villmann
2009 - Abstracts, Technical Reports
W. Arlt, S. Hahner, R. Libe, B.A. Hughes, M. Biehl, H. Stiekema, P. Schneider, et al.,
T. Geweniger, P. Schneider, F.-M. Schleif, M. Biehl, T. Villmann
M. Biehl, B. Hammer, F.-M. Schleif, P. Schneider, T. Villmann
2008 - Journal Articles
N. Petkov, E. Alegre, M. Biehl, and L. Sanchez
A. Witoelar, M. Biehl, A. Ghosh, B. Hammer,
2008 - Conference contributions
M. Strickert, P. Schneider, J. Keilwagen, T. Villmann, M. Biehl, B. Hammer
M. Strickert, K. Witzel, J. Keilwagen, H.P. Mock, P. Schneider, M. Biehl, T. Villmann
P. Schneider, F.-M. Schleif, T. Villmann, M. Biehl
A. Witoelar, A. Ghosh, M. Biehl
2008 - Abstracts, Technical Reports, Editorials
P. Schneider, K. Bunte, H. Stiekema, B. Hammer, T. Villmann, M. Biehl
K. Bunte, P. Schneider, B. Hammer, F.-M. Schleif, T. Villmann, M. Biehl
P. Schneider, M. Biehl, B. Hammer
A. Witoelar, M. Biehl
F. Rossi, C. Angulo Bahon, M. Biehl
Publications in 2007
2007 - Journal Articles
M. Biehl, A. Ghosh, and B. Hammer
2007 - Conference Contributions
M. Biehl, R. Breitling, Y. Li,
A. Witoelar, M. Biehl, and B. Hammer
P. Schneider, M. Biehl, F.-M. Schleif, B. Hammer
P. Schneider, M. Biehl, and B. Hammer
A. Witoelar, M. Biehl, A. Ghosh, and B. Hammer
N. Petkov, E. Alegre, M. Biehl, and L. Sanchez
2007 - Edited volumes, special issues
M. Biehl, E. Merenyi, F. Rossi,
M. Biehl, B. Hammer, M.Verleysen, T. Villmann
Publications in 2006
2006 - Journal Articles
A. Ghosh, M. Biehl, and B. Hammer
M. Biehl, A. Ghosh, and B. Hammer
2006 - Conference Contributions
M. Biehl, P. Pasma, M. Pijl, L. Sanchez, and N. Petkov
2006 - Technical Reports
M. Biehl, B. Hammer, and P. Schneider
Publications in 2005
2005 - Journal Articles
2005 - Conference Contributions
A. Ghosh, M. Biehl, and B. Hammer
C. Bunzmann, M. Biehl, and R. Urbanczik
M. Biehl, A. Ghosh, and B. Hammer
Publications in 2004
A. Ghosh, M. Biehl, A. Freking, G. Reents
For publications before 2004, click
here.
Marker
Selection for the Detection of Trisomy 21 Using Generalized Matrix Learning Vector Quantization
(IEEE
Explore)
(RUG provided
preprint version)
In: IJCNN 2017, Proc. of the International Joint Conference on Neural Networks (Anchorage, Alaska), IEEE, pp. 3704-3708, 2017
Biomedical
data analysis in translational research: Integration
of expert knowledge and interpretable models
In: ESANN 2017,
Proc. of the 25th European Symposium on Artificial
Neural Networks (Bruges, Belgium), M. Verleysen (editor)
Ciaco-i6doc.com, 177-186, 2017
Comparison
of strategies to learn from imbalanced classes for
computer aided diagnosis of inborn steroidogenic disorders
In: ESANN 2017,
Proc. of the 25th European Symposium on Artificial
Neural Networks (Bruges, Belgium),
M. Verleysen (editor),
Ciaco-i6doc.com, pp. 199-204, 2017
Prototype
based models for the supervised learning of classificaton schemes
In: IAU 12,
Proc. of the International Astronomical Union 12 (S325): 129-138,
2017
Doi: 10.1017/S1743921316012928
14th
SC@RUG 2016-2017, Proc. of the 14th
Student Colloquium Computer Science
University of Groningen, 88 pages, 2017.
Abstract:
Lifelong (machine) learning of drifting concepts in prototype-based
classifiers
In: F-M. Schleif, T. Villmann (eds.), MLR-01-2017,
MiWoCI Workshop, Mittweida/Germany (2017)
Computer
aided diagnosis under the influence of heterogeneous
data and imbalanced classes (extended abstract)
Presented at ICT.OPEN 2017, Amersfoort
Publications in 2016
2016 - Journal Publications
Differentiating
Early and Late Stage Parkinson's Disease Patients from Healthy Controls
JBEMi, Journal of Biomedical Engineering and Medical Imaging 3(6): 33-43 (2016)
Prototype-based
models in machine learning
Advanced Review in
WIRES Cognitive Science, 7(2):92-111, 2016
doi: 10.1002/wcs.1378
Odor recognition in robotics applications by discriminating time-series
modeling
preprint version of Pattern Analysis and Applications 19(1): 207-220 (2016),
available on-line (2015)
doi:10.1007/s10044-014-0442-2
Classification
of FDG-PET Brain Data by Generalized Matrix Relevance LVQ
In: BrainComp 2015,
Proc. International Workshop
on Brain Inspired Computing, BrainComp 2015,
K. Amunts, L. Grandinetti, T. Lippert, N. Petkov (eds.),
Springer LNCS 10087, 131-141 (2016)
doi 10.1007/978-3-319-50862-7_10
Functional
approximation for the classification of smooth time series
In: NC^2 2016, Proc. Workshop on New Challenges in Neural Computation 2016,
B. Hammer, T. Martinetz, T. Villmann (eds.),
Machine Learning Reports Vol. 04/2016, 24-31 (2016)
The
sugar dataset -
A multimodal hyperspectral dataset for classication and research
In: F.-M. Schleif, T. Villmann (eds), Proc. MIWOCI 2016
Machine Learning Reports 03/2016, 15-18.
Predicting
recurrence in clear cell Renal Cell Carcinoma
In:CEC 2016, Proc. Congress on Evolutionary Computation,
IEEE (2016)
doi: 10.1109/CEC2016.7743855
Towards
Emotion Classification Using Appraisal Modeling
In: Psychology and Mental Health - Concepts, Methodologies, Tools, and Applications.
IGI Global, Chapter 23, pp. 552-572, 2016
Reprinted version of : Int. J. of Synthetic Emotions 6(1): 40-59 (2016)
Prototype-based
classification for image analysis and its application to crop disease
diagnosis
In: WSOM 2016,
Advances in Self-Organizing Maps and Learning
Vector Quantization
Proc. of the 11th International Workshop WSOM, Houston, Texas,
E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.),
Springer Series on
Adv. in Intelligent Systems and Computing, Vol. 428: pp. 329-339, 2016
doi: 10.1007/978-3-319-28518-4_29
Functional Representation of Prototypes in
LVQ and Relevance Learning
In: WSOM 2016,
Advances in Self-Organizing Maps and Learning
Vector Quantization
Proc. of the 11th International Workshop WSOM, Houston, Texas
E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.),
Springer Series on
Adv. in Intelligent Systems and Computing, Vol. 428: pp. 317-327, 2016
doi: 10.1007/978-3-319-28518-4_28
LVQ
and SVM Classification of FDG-PET Brain Data
In: WSOM 2016
Advances in Self-Organizing Maps and Learning
Vector Quantization
Proc. of the 11th International Workshop WSOM, Houston, Texas
E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.)
Springer Series on
Adv. in Intelligent Systems and Computing, Vol. 428: pp. 205-215, 2016
doi: 10.1007/978-3-319-28518-4_18
Complex Variants of GLVQ Based on Wirtinger's
Calculus
In: WSOM 2016 Advances in Self-Organizing Maps and Learning
Vector Quantization
Proc. of the 11th International Workshop WSOM, Houston, Texas.
E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.)
Springer Series on
Adv. in Intelligent Systems and Computing, Vol. 428: pp. 293-303, 2016
doi: 10.1007/978-3-319-28518-4_26
Funktionale Approximation von Spektraldaten zur Steigerung
der Klassifikationsleistung in GMLVQ
In: M. Schenk (ed.),
Arbeitssysteme der Zukunft,
17. Forschungskolloquium am Fraunhofer IFF 2015, Magedburg
pp. 49-54, 2016
13 th
SC@RUG 2015-2016, Proc. of the Student Colloquium Computer Science
University of Groningen, 60 pages, 2016
Functional
Representation of Prototypes in LVQ and Relevance Learning
Proc. BNAIC 2016, Amsterdam, pp. 165-166, 2016
The
urinary steroid metabolome as a non-invasive
tool to stage non-alcoholic fatty liver disease
Endocrine Abstracts 44: OC1.4 (2016)
doi: 10.1530/endoabs.44.OC1.4
Steroid
metabolomics for diagnosis of inborn steroidogenic disorders – bridging the gap between clinician and scientist through computational approaches
Endocrine Abstracts 44: P40 (2016)
doi: 10.1530/endoabs.44.P40
Urine steroid metabolomics is a highly sensitive tool for post-operative recurrence detection in adrenocortical carcinoma
Endocrine Abstracts 41: OC1.4 (2016)
doi: 10.1530/endoabs.41.OC1.4
The changing steroid metabolome across the spectrum of non-alcoholic fatty liver disease
Endocrine Abstracts 41: GP173 (2016)
doi: 10.1530/endoabs.41.GP173
Publications in 2015
2015 - Journal Articles
Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution
Front. Comput. Neuroscience 9, 2015
doi:10.10.3389/fncom.2015.00110
Expression of chemokines CXCL4 and CXCL7 by synovial macrophages defines
early stage of rheumatoid arthritis
Ann. of the Rheumatic Disease 75: 763-771, 2016 (online 2015)
doi:10.1136/annrheumdis-2014-206921
MED-NODE: A computer-assisted melanoma diagnosis system using
non-dermoscopic images
Expert Systems with Applications 42(19): 6578-6585, 2015
doi:10.1016/j.eswa.2015.04.034
Towards
Emotion Classification Using Appraisal Modeling
Int. J. of Synthetic Emotions 6(1): 40-59, 2015
doi:10.4018/IJSE.2015010103
Insightful Stress Detection from Physiology Modalities using Learning Vector Quantization
Neurocomputing 151 (2): 873-882 (2015)
doi:10.1016/j.neucom.2014.10.008
Non-Euclidean Principal Component Analysis by Hebbian Learning
Neurocomputing 147: 107-119, 2015
doi:10.1016/j.neucom.2013.11.049
Inferring
Feature Relevances From Metric Learning
In:
IEEE SSCI 2015, Symposium Series on Computational Intelligence, pp. 1599-1606, 2015
doi: 10.1109/SSCI.2015.225
Polynomial
Approximation of Spectral Data in LVQ and Relevance Learning
In: NC^2,New Challenges in Neural Computation, Workshop at the GCPR, Aachen
Machine Learning Reports 03-2015: 25-32, 2015
Facial
Expression Recognition using Learning Vector Quantization
In: G. Azzopardi and N. Petkov (eds.): CAIP 2015, Part II.
16th Intl. Conf. on Computer Analysis of Images and Patterns
Springer LNCS 9257, pp. 760-771, 2015
doi:10.1007/978-3-319-23117-4_65
Learning
Vector Quantization with Adaptive Cost-based Outlier-Rejection
In: G. Azzopardi and N. Petkov (eds.): CAIP 2015, Part II.
16th Intl. Conf. on Computer Analysis of Images and Patterns
Springer LNCS 9257, pp. 772-782, 2015
doi:10.1007/978-3-319-23117-4_66
Stationarity of Matrix Relevance LVQ
In:International Joint
Conference on Neural Networks (IJCNN 2015),
doi:10.1109/IJCNN.2015.7280441
Combining
dissimilarity measures for prototype-based classification
In: ESANN 23,
Proc. of the 23rd European Symposium on Artificial
Neural Networks, M. Verleysen (editor)
d-side publishing, 31-36, 2015
Developments
in computational intelligence and machine learning
Editorial, Special Issue, ESANN 2014 - Selected Papers,
Neurocomputing 169:185-186, 2015
doi:10.1016/j.neucom.2015.03.062
12th SC@RUG 2014-2015, Proc. of the Student Colloquium Computer Science
University of Groningen, 74 pages, 2015.
Further advances in diagnosis of adrenal cancer: a high-throughput
urinary steroid profiling method using LC-MS.
Endocrine Abstracts 38: OC2.3 (2015)
doi: 10.1530/endoabs.38.OC2.3
Urine steroid metabolomics as a diagnostic tool in primary aldosteronism.
Endocrine Abstracts 38: OC1.6 (2015)
doi:10.1530/endoabs.38.OC1.6
Urine steroid metabolomics as a novel diagnostic tool for early detection
of recurrence in adrenocorticla carcinoma.
Endocrine Abstracts 38: OC3.4 (2015)
doi:10.1530/endoabs.38.OC3.4
Publications in 2014
2014 - Journal Articles
The somatic genomic landscape of chromophobe renal cell carcinoma
Cancer Cell 26(3): 319-330, 2014
R. Norel, A. Iskandar,
E. Blaese, J.J. Rice, M.C. Peitsch, J. Hoeng, G. Stolovitzky, L.G. Alexopoulos, C. Poussin
(*) including M. Biehl
A crowd sourcing approach for the construction of species specific cell signaling networks
Bioinformatics 31(4): 484-491, 2015 (online 2014, open access)
doi:10.1093/bioinformatics/btu659
Predicting protein phosphorylation from gene expression: top methods from the IMPROVER species translation challenge
Bioinformatics 31(4): 462-470 (2015, online in 2014)
open access
doi: 10.1093/bioinformatics/btu490
Inter-species inference of gene set enrichment in lung-epithelial cells from
proteomic and large transcriptomics data sets
Bioinformatics 31(4): 492-500 (2015, online in 2014)
open
access
doi: 10.1093/bioinformatics/btu569
Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge
Bioinformatics 31(4): 453-461 (2015, online in 2014)
open access
doi: 10.1093/bioinformatics/btu407
Segmented
shape-symbolic time series representation
Valid Interpretation of
Feature Relevance for Linear Data Mappings. accepted contribution to the IEEE
IEEE CIDM 2014,
Symp. on Computational Intelligence and Data Mining, pp 149-156, 2014
doi: 10.1109/CIDM.2014.7008661
Distance measures for prototype based classification
Preprint version of an invited contribution to:
BrainComp 2013,
Proc. of the
International Workshop on Brain-Inspired Computing,
Cetraro/Italy, July 2013
L. Grandinetti, T. Lippert, N. Petkov (eds.), Springer Lecture Notes Vol. 8603, pp. 100-116 (2014)
11th
SC@RUG 2013-2014, Proc. of the Student Colloquium Computer Science
University of Groningen, 72 pages, 2014.
Statistical
Quality Measures and ROC-Optimization by Learning Vector Quantization
In:
H.A. Kestler, M. Schmid, L. Lausser, J.M. Krauss (eds.),
Statistical Computing 2014, Universität Ulm, pp. 2-6 (2014)
Prototype-based classifiers and their application in the life sciences (abstract)
In: WSOM 2014,
T. Villmann, F.-M. Schleif, M. Kaden, and M. Lange (editors)
Advances in Self-Organizing Maps and Learning Vector Quantization: Proc. of
the 10th International Workshop, WSOM 2014
Springer, Advances in Intelligent
Systems and Computing Vol. 295, p. 121, 2014.
ROC-optimization and statistical
quality measures in Learning Vector Quantization.
In: F.-M. Schleif and T. Villmann (editors)
MIWOCI 2014, Mittweida Workshop on Computational Intelligece,
volume MLR-2014-01 of Machine Learning Reports, pages 23-34. Univ.
of Bielefeld (2014)
Interpretation of linear mappings
employing L1-regularization (abstract).
In: F.-M. Schleif and T. Villmann (editors)
MIWOCI 2014, MittweidaWorkshop on Computational Intelligence,
volume
MLR-2014-01 of Machine Learning Rep. , page 1. Univ. of Bielefeld (2014)
Publications in 2013
2013 - Journal Articles
Adaptive Matrices and Filters for Color Texture Classification
J. of Math. Imaging and Vision 47: 79-92, 2013
(Springer, open access since May 2012)
doi:10.1007/s10851-012-0356-9
Assessment
of acrosome state in boar spermatozoa heads using n-contours descriptor and RLVQ
preprint version of:
Computer Methods and Programs in Biomedicine 111: 525-536, 2013
(final version available online)
doi:10.1016/j.cmpb.2013.05.003
Analysis of
Flow Cytometry Data by Matrix Relevance Learning Vector Quantization
PLOS One 8: e59401 (2013),
(open access)
doi:10.1371/journal.pone.0059401
(* including M. Biehl, K. Bunte, P. Schneider)
Critical
assessment of automated flow cytometry data analysis techniques
Nature Methods 10: 228-238 (2013), article and supplementary material
available online
Regularization and improved interpretation of linear data mappings and adaptive distance measures
Preprint version of a contribution to:IEEE CIDM 2013,
Symp. on Computational Intelligence and Data Mining
Proc. IEEE SSCI 2013.
Article
available online, 2013
Non-Euclidean
Independent Component Analysis and Oja's Learning
In: ESANN 21, Proc. Europ. Symp. Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, pp. 125-130, 2013
Non-Euclidean Principal
Component Analysis and Oja's Learning Rule - Theoretical Aspects
In: WSOM 2012,
Advances in Self-Organizing Maps,
Proc. 9th Workshop on Self-Organizing Maps WSOM 2012, Santiago/Chile,
Springer, Advances in Intelligent Systems and Computing Vol. 198, pp. 23-33, 2013
doi: 10.1007/978-3-642-35230-0_3
Publications in 2012
2012 - Journal Articles
Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization
Künstliche Intelligenz, KI 26: 391-395, 2012
(Springer, open access)
Artificial Intelligence in Medicine 56: 91-97 (2012)
(Elsevier, available online)
Functional Relevance Learning in Generalized Learning Vector Quantization
preprint version of: Neurocomputing 90: 85-95, 2012
(available online), doi: 10.1016/j.neucom.2011.11.029
Stochastic Neighbor Embedding (SNE) for Dimension Reduction and
Visualization using arbitrary Divergences
preliminary version of: Neurocomputing 90: 23-45, 2012
(available online), doi: 10.1016/j.neucom.2012.02.034
Limited rank matrix learning - discriminative dimension reduction and
visualization
preprint-version of: Neural Networks 26: 159-173 (2012)
(available online), doi: 10.1016/j.neunet.2011.10.001
A general framework for dimensioniality reducing data visualization mapping
preprint version of: Neural Computation 24: 771-804 (2012)
(available online), doi: 10.1162/NECO_a_00250
Differentiable Kernels in Generalized Matrix Learning Vector Quantization
Proc. 11th Intl. Conf. on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA, 2012,
Volume 1, pp. 132-137, 2012
IEEE, available online
Large Margin Discriminative Linear Visualization by Matrix Relevance Learning
Proc. Intl. Joint Conference on Neural Networks IJCNN 2012, Brisbane/Australia,
pp. 1873-1880 (2012)
IEEE, available online
Visualization of processes in self-learning systems
Proc. Tenth Annual Conf. Privacy, Security, and Trust, Paris 2012, pp. 244-249 (2012)
IEEE, available online
Visualizing the quality of dimensionality reduction
preprint version, Proc. 20th Europ. Symp. Artificial Neural Networks (ESANN),
M. Verleysen (ed.),
d-side publishing, 179-184 (2012)
Adaptive learning for complex-valued data
preprint version, Proc. 20th Europ. Symp. Artificial Neural Networks (ESANN),
M. Veleysen (ed.), d-side publishing, 381-386 (2012)
Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors
preprint version, Proc. 20th Europ. Symp. Artificial Neural Networks (ESANN),
M. Verleysen (ed.), d-side publishing, 423-428 (2012)
9th
SC@RUG 2011-2012, Proc. of the Student Colloquium Computer Science
University of Groningen, 57 pages, 2012.
Publications in 2011
2011 - Journal Articles
E. Porfiri, G. Opocher, J. Bertherat, F. Mantero, B. Allolio, M. Terzolo, P. Nightingale,
C.H.L. Shackleton, X. Bertagna, M. Fassnacht, P.M. Stewart
Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors
J. of Clinical Endocrinology & Metabolism,
JCEM 96: 3775-3784, 2011
available online
Learning Effective Color Features for Content Based Image
Retrieval in Dermatology
Pattern Recognition 44 (2011) 1892-1902.
available online at Science Direct.
Neighbor Embedding XOM for Dimension Reduction and Visualization
Neurocomputing, 74 (2011) 1340-1350.
Divergence based classification in Learning Vector Quantization
preprint version of: Neurocomputing, 74 (2011) 1429-1435.
Adaptive Matrices for Color Texture Classification
preliminary version of a paper which appeared
in: P. Real, D. Diaz-Pernil, H. Molina-Abril, A. Berciano, W.G. Kropatsch (eds.),
CAIP 2011, Proc. Part II, Computer Analysis of Images and Patterns - 14th International Conference, Seville, Spain,
Springer LNCS Vol.6855, 489-497, 2011
A
general framework for dimensionality reduction for large data sets
In: WSOM 2011, Advances in Self-Organizing Maps, 8th Workshop
on Self-Organizing Maps,
J. Laaksonen, T. Honkela (eds.),
Springer
LNCS Vol. 6731, 277-287, 2011
Learning of Causal Relations
In: ESANN 2011, Proc. 19th Europ. Symp. on Artificial Neural Networks,
tutorial paper for a special session,
M. Verleysen (ed.), d-side publishing, 287-296, 2011
Multivariate class labeling in Robust Soft LVQ
In: ESANN 2011,Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 17-22, 2011
Causal Relevance learning for robust classification under interventions
In: ESANN 2011, Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 315-320, 2011
Generalized Functional Relevance Learning Vector Quantization
In: ESANN 2011, Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 93-98, 2011
Supervised dimension reduction mappings
In: ESANN 2011, Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 281-286, 2011
Dimensionality reduction maps
preprint version of an article in: SSCI CDIM 2011,
Proc. IEEE Symp. on Computational
Intelligence and Data Mining, Paris,
Pages 349-356, 2011
doi: 10.1109/CIDM.2011.5949443
Texture Feature Selection with Relevance Learning to Classify Interstitial Lung Disease Patterns
in: Medical Imaging 2011: Computer Aided Diagnostics, R.M. Summers, B. van Ginneken (eds.),
SPIE Conference Proceedings Vol. 7963 (43), 8 pages, 2001
doi: 10.1117/12.877894
8th
SC@RUG 2010-2011, Proc. of the Student Colloquium Computer Science
University of Groningen, 123 pages, 2011.
Waypoint averaging and step size control in learning by gradient descent
Technical Report, In: MIWOCI 2011, Mittweida Workshop on
Computational Intelligence
Machine Learning Reports, MLR-2011-06: 16-26, 2011
available online,
How to evaluate Dimensionality Reduction?
In: NC^2 2011, Proc. Workshop New Challenges in Neural Computation,
B. Hammer and T. Villmann (eds.), pp. 29-37, 2011,
available online,
About Sparsity in Functional Relevance Learning in
Generalized Learning Vector Quantization
Technical Report, Machine Learning Reports, MLR-2011-03,
pdf available
online,
2011
Publications in 2010
2010 - Journal Articles
Window-based example selection in Learning Vector Quantization
preprint version of:
Neural Computation 22: 2924-2961 (2010)
Post-correlation radio frequency interference classification methods
Monthly Notices of the Royal Astronomical Society
405: 155-167 (2010)
Regularization in Matrix Relevance Learning
preprint version of: IEEE Trans. Neural Networks 21: 831-840 (2010)
online: IEEE Xplore
Adaptive
Local Dissimilarity measures for Discriminative
Dimension Reduction of Labeled Data
preprint version of: Neurocomputing 73: 1074-1092 (2010).
Hyperparameter
Learning in Probabilistic Prototype-Based Models
preprint version of:
Neurocomputing 73: 1117-1124 (2010)
The LOFAR RFI detection pipeline and its first results
RFI mitigation workshop RFI2010, Groningen, March 2010, to be
published in Proceedings of Science
(2010)
Generalized derivative
based kernelized Learning Vector Quantization
In: Intelligent Data Engineering and Automated Learning - IDEAL 2010,
C. Fyfe, P. Tino, D. Charles, C. Garcia-Osorio, H. Yin (eds.),
Springer LNCS 6283, 21-28 (2010)
Divergence Based Learning Vector Quantization
preprint version of:
18th Europ. Symp. on Artificial Neural Networks, ESANN 2010,
M. Verleysen (ed.), d-side publishing, 247-252 (2010)
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence
for Dimensionality Reduction and Visualziation
preprint version of:
18th Europ. Symp. on Artificial Neural Networks, ESANN 2010,
M. Verleysen (ed.), d-side publishing, 87-92 (2010).
The Mathematics of Divergence Based Online Learning in Vector Quantization
in: Artificial Neural Networks in Pattern Recognition
(ANNPR 2010),
F. Schwenker, N. El Gayar (eds.), Springer LNAI Vol. 5998, 108-119 (2010).
7th
SC@RUG 2009-2010, Proc. of the Student Colloquium Computer Science
University of Groningen, 46 pages, 2010.
Some steps towards a general principle for
dimensionality reduction mappings
In: Dagstuhl Seminar Proceedings 10302, B. Hammer, P. Hitzler, W. Maass,
M. Toussaint (eds.), available
online,
2010
Early Nervous Systems: Theoretical background and a preliminary model
of neuronal processes
Extended abstract, to appear in Proc. Artificial Life XII, Odense/Denmark,
August 2010.
Development of urinary steroid profiling as a high-throughput screening
tool for the detection of malignancy in patients with adrenal tumours
Abstract, British Endocrine Society, Endocrine Abstracts 21, OC3.3 (2010)
Adaptive Relevance
Matrices in Learning Vector Quantization
preprint version of Neural Computation 21: 3532-3561 (2009)
journal article availble on-line at
MIT Press (2009)
Distance
learning in discriminative vector quantization
preprint version of: Neural Computation 21, 2942-2969 (2009)
journal article available on-line at
MIT Press (2009)
Phase
transitions in vector quantization and neural gas
preprint version of: Neurocomputing 72, 1390-1397 (2009)
Metric Learning for Prototype-based classification
preprint version of a contribution to: Innovations in Neural Information Paradigms and Applications
M. Bianchini, M. Maggini, F. Scarselli, L.C. Jain (eds.),
Springer Studies in Computational Intelligence, Vol 247 (2009), 183-199
available on-line
(Springer Link)
Some Theoretical Aspects of the Neural Gas Vector Quantizer
in:
Similiarity based clustering
M. Biehl, B. Hammer, M. Verleysen, T. Villmann (eds.)
Springer Lecture Notes Artificial Intelligence Vol. 5400, 23-34 (2009)
Statistical Mechanics of On-line learning
preprint version of an article in:
Similiarity based clustering
M. Biehl, B. Hammer, M. Verleysen, T. Villmann (eds.)
Springer Lecture Notes Artificial Intelligence Vol. 5400, 1-22 (2009)
Nonlinear Dimension Reduction and Visualization
of Labeled Data
preprint version of a contribution to: Computer Analysis of Images
and Patterns, CAIP 2009 (Münster/Germany)
X. Jiang, N. Petkov (eds.),
Springer LNCS 5702 (2009), 1162-1170
available on-line at
(Springer Link)
Matrix metric adaptation for improved linear discriminant analysis of biomedical data
in: IWANN 2009 (Part I), J. Cabestany et al. (eds.), Springer LNCS 5517, 933-940 (2009)
available on-line
(Springer Link)
Hyperparameter
Learning in Robust Soft LVQ
preprint version of an article published in:
17th Europ. Symp. on Artificial Neural Networks, ESANN 2009,
M. Verleysen (ed.), d-side publishing, 517-522 (2009)
Equilibrium
properties of offline LVQ
preprint version of an article published in:
17th Europ. Symp. on Artificial Neural Networks, ESANN 2009,
M. Verleysen (ed.), d-side publishing, 535-540 (2009)
Adaptive Metrics for Content
Based Image Retrieval in Dermatology
preprint version of an article published in:
17th Europ. Symp. on Artificial Neural Networks, ESANN 2009,
M. Verleysen (ed.), d-side publishing, 129-134 (2009)
Nonlinear Discriminative
Data Visualization
preprint version of an article published in:
17th Europ. Symp. on Artificial Neural Networks, ESANN 2009,
M. Verleysen (ed.), d-side publishing, 65-70 (2009)
Similiarity based clustering
Springer Lecture Notes Artificial Intelligence Vol. 5400/2009
Andvances in machine learning and computational intelligence
Special issue: Neurocomputing 72, Editorial: pages 1377-1378 (2009)
Similarity-based
learning on structures
Summary and abstract collection, Dagstuhl Seminar Proceedings 09081, 2009.
Urinary steroid profiling as a biomarker tool for the detection of adrenal malignancy - Results of the EURINE ACC study
Abstract, British Endocrine Society, Endocrine Abstracts 19, OC14 (2009)
Extending
RSLVQ to handle data points with uncertain class assignments
Machine Learning Reports 02/2009, Univ. Leipzig (2009)
Stationarity
of Matrix Relevance Learning Vector Quantization
Machine Learning Reports 01/2009, Univ. Leipzig (2009)
Publications in 2008
Automatic classification of the acrosome status of boar
spermatozoa using digital image processing and LVQ
Computers in Biology and Medicine 38: 461-468 (2008)
Learning Dynamics of Neural
Gas and Vector Quantization
preprint version of Neurocomputing 71: 1210-1219 (2008)
Discriminatory data mapping by matrix-based supervised
learning metrics
in: Proc. Third International Workshop on Artificial Neural Networks in
Pattern Recognition, ANNPR 2008, Paris/France,
L. Provost, S. Marinai, F. Schwenker (eds.), Springer LNCS 5064: 78-89
(2008)
available on-line
(Springer Link)
Adaptive Matrix Metrics for attribute
dependence analysis in differential high-throughput data
Proc. 5th Int. Workshop on Computational Systems Biology,
WCSB 2008, Leipzig/Germany,
M. Ahdesmäki et al. (eds.), Tampere Intl. Center for Signal Processing, TICSP Vol. 41: 181-184 (2008)
Generalized Matrix Learning Vector Quantizer for the Analysis
of Spectral Data
preprint version of an article published in:
16th Europ. Symp. on Artificial Neural Networks, ESANN 2008,
M. Verleysen (ed.), d-side publishing (2008) 451-456.
Phase
Transitions in Vector Quantization
preprint version of an article published in:
16th Europ. Symp. on Artificial Neural Networks, ESANN 2008,
M. Verleysen (ed.), d-side publishing (2008) 221-226.
Regularization
in Matrix Relevance Learning
Machine Learning Reports, Report 02/2008, Univ. Leipzig
Discriminative Visualization by
Limited Rank Matrix Learning
Machine Learning Reports, Report 03/2008, Univ. Leipzig
Matrix
adaptation in discriminative vector quantization
IfI Technical Report Series, IFI-08-08, TU Clausthal (2008)
Equilibrium physics approach in vector quantization
Part I: General
formalism and high temperature limit
Part II:
Annealed approximation (preliminary version)
Progress in modeling, theory, and application of computational intelligence
(link to the journal page through science direct),
Editorial of a special issue,
Neurocomputing 71: 1117-1119, 2008
Dynamics and generalization ability
of LVQ algorithms
preliminary version of a paper in
J. Machine Learning Research 8 (Feb):323-360, 2007
published version available from the
JMLR web page
Analysis
of Tiling Microarray Data by Learning Vector Quantization
and Relevance Learning
in: Proc. 8th Intl. Conf. on Intelligent
Data Engineering and Automated Learning,
Birmingham/UK, Dec. 2007, ((IDEAL 2007)),
Y. Yin et. al (eds),
Springer Lecture Notes in Computer Science Vol. 4881, 880-889 (2007)
availiable on-line (Springer Link)
Learning
Vector Quantization: generalization ability and
dynamics of competing prototypes
Workshop on Self-Organizing Maps 2007
in Bielefeld (WSOM 2007),
proceedings published on CD (Univ. Bielefeld, 2007)
Advanced
metric adaptation in Generalized LVQ for
classification of mass sepctrometry data
Workshop on Self-Organizing Maps 2007
in Bielefeld (WSOM 2007),
proceedings published on CD (Univ. Bielefeld, 2007)
Relevance
Matrices in Learning Vector Quantization
In: ESANN 2007, Proc.
15th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 37-42, 2007
On
the Dynamics of Vector Quantization and Neural Gas
In: ESANN 2007,Proc.
15th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, pp. 127-132, 2007
LVQ acrosome integrity assessment of
boar sperm cells
preprint version of a conference contribution in:
Proc. CompImage 2006, Coimbra/Portugal,
eds. J.M.R.S. Taveres and R.M.N. Jorge, Taylor and Francis,
6 pages, 2007
Advances in computational intelligence and learning
Editorial of a special issue,
Neurocomputing 70: 1117-1119, 2007
doi: 10.1016/j.neucom.2006.12.001
Similarity-based
clustering and its application to medicine and biology
Summary and abstract collection, Dagstuhl Seminar Proceedings 07131, 2008.
Performance analysis of LVQ algorithms: a statistical physics approach
preprint version of an article in:
Neural Networks 19: 817, 2006
doi: 10.1016/j.neunet.2006.05.010
Learning Vector Quantization: the dynamics
of Winner-Takes-All algorithms
preprint version of an article in:
Neurocomputing 69 (7-9): 660-670, 2006
doi: 10.1016/j.neucom.2005.12.007
Classification of Boar Sperm Head Images using
Learning Vector Quantization
In: ESANN 2006, Proc.
European Symposium on Artificial Neural Networks
in Bruges/Belgium, April 2006
M. Verleysen (ed.), d-side publishing, pp. 545-550, 2006
Matrix Learning in Learning Vector Quantization
Ifl Technical Report Series, Ifl-06-14, TU Clausthal, 2006.
Dynamical Analysis of LVQ type learning rules
preprint version of a paper in:
Proc. of the 5th Workshop on
Self-Organizing Maps WSOM'05, Univ. de Paris (I), 2005.
Efficient training of multilayer perceptrons using
principal component analysis
preprint version of Phys. Rev. E 72: 026117 (2005)
The dynamics of Learning Vector Quantization
In: ESANN 2005, Proc.
European Symposium on Artificial Neural Networks
in Bruges/Belgium, April 2005
M. Verleysen (ed.), d-side publishing, pp. 13-18, 2005
A theoretical framework for analyzing the
dynamics of learning vector quantization
Technical Report 2004-9-02, Mathematics and
Computing Science, University Groningen, 2004.
doi: 10.1103/PhysRevB.69.165303