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link to publications before 2004
in case of problems with downloading files, contact m.biehl _at_ rug.nl


M. Biehl:   publications since 2004
(preprints mostly available as pdf and/or ps)


Publications in 2017

2017 - Journal publications

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) 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: Proc. of the 12th Intl.Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Visualization (WSOM+), Nancy/France, 2017
IEEE, in press.

M. LeKander, M. Biehl, H. de Vries.
Empirical Evaluation of Gradient Methods for Matrix Learning Vector Quantization.
In: Proc. of the 12th Intl.Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Visualization (WSOM+), Nancy/France, 2017
IEEE, in press.

G. Bani, U. Seiffert, M. Biehl, F. Melchert
Adaptive Basis Functions for Prototype-based Classification of Functional Data.
In: Proc. of the 12th Intl. Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Visualization (WSOM+) Nancy/France, 2017
IEEE, in press.

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: Proc. of the 12th Intl. Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Visualization (WSOM+) Nancy/France, 2017
IEEE, in press.

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: Proc. Intl. Conf. on Artificial Intelligence and Soft Computing ICAISC 2017.
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
Marker Selection for the Detection of Trisomy 21 Using Generalized Matrix Learning Vector Quantization
(IEEE Explore)     (RUG provided version)
In: Proc. of the International Joint Conference on Neural Networks IJCNN 2017 (Anchorage, Alaska), IEEE, pp. 3704-3708, 2017

G. Bhanot, M. Biehl, T. Vilmmann, D. Zühlke
Biomedical data analysis in translational research: Integration of expert knowledge and interpretable models
In: M. Verleysen (editor), Proc. of the 25th European Symposium on Artificial Neural Networks ESANN 2017 (Bruges, Belgium), Ciaco-i6doc.com, 177-186 (2017)

S. Ghosh, E.S. Baranowski, R. van Veen, G.-J. de Vries, M. Biehl, W. Arlt, P. Tino, K. Bunte
Comparison of strategies to learn from imbalanced classes for computer aided diagnosis of inborn steroidogenic disorders
In: M. Verleysen (editor), Proc. of the 25th European Symposium on Artificial Neural Networks (ESANN 2017) (Bruges, Belium), Ciaco-i6doc.com, 177-186 (2017)

M. Biehl, B. Hammer, T. Villmann
Prototype based models for the supervised learning of classificaton schemes
In: Proceedings of the International Astronomical Union 12 (S325): 129-138 (2017)
Doi: 10.1017/S1743921316012928

2017 - Technical reports, abstracts, other publications

M. Biehl, F. Abadi, C. Göpfert, B. Hammer
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)

R. Smedinga, M. Biehl, F. Kramer (editors)
14th SC@RUG 2016-2017, Proc. of the 14th Student Colloquium Computer Science
University of Groningen, 88 pages, 2017.

S. Ghosh, E.S. Baranowski, R. van Veen, G.-J. de Vries, M. Biehl, W. Arlt, P. Tino, K. Bunte
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

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
Differentiating Early and Late Stage Parkinson's Disease Patients from Healthy Controls
Journal of Biomedical Engineering and Medical Imaging 3(6): 33-43 (2016)

T. Villmann, M. Kaden, W. Herrmann, M. Biehl
Learning Vector Quantization classifiers for ROC-optimization
Computational Statistics, available online (2016)
doi: 10.1007/s00180-016-0678-y

M. Biehl, B. Hammer, T. Villmann
Prototype-based models in machine learning
Advanced Review in WIRES Cognitive Science, available online (2016)
doi: 10.1002/wcs.1378

F.-M. Schleif, B. Hammer, J.G. Monroy, J.G. Jimenez, J.-L. Blanco-Claraco, M. Biehl, N. Petkov
Odor recognition in robotics applications by discriminating time-series modeling
Pattern Analysis and Applications 19(1): 207-220 (2016), available on-line (2015)
doi:10.1007/s10044-014-0442-2

2016 - Conference contributions and book chapters

M. Biehl, D. Mudali, K.L. Leenders, J.B.T.M. Roerdink
Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ
In: K. Amunts, L. Grandinetti, T. Lippert, N. Petkov (eds.), Proc. International Workshop on Brain Inspired Computing, BrainComp 2015, Springer LNCS 10087, 131-141 (2016)
doi 10.1007/978-3-319-50862-7_10

F. Melchert, U. Seiffert, M. Biehl
Functional approximation for the classification of smooth time series
In: B. Hammer, T. Martinetz, T. Villmann (eds.),
Proc. Workshop on New Challenges in Neural Computation 2016,
Machine Learning Reports Vol. 04/2016, 24-31 (2016)

F. Melchert, A. Matros, M. Biehl, U. Seiffert
The sugar dataset - A multimodal hyperspectral dataset for classi􏰁cation and research
In: F.-M. Schleif, T. Villmann (eds), Proc. MIWOCI Workshop 2016
Machine Learning Reports 03/2016, 15-18.

G. Murkherjee, G. Bhanot, K. Raines, S. Sastry, S. Doniach, M. Biehl
Predicting recurrence in clear cell Renal Cell Carcinoma
In: Proc. Congress on Evolutionary Computation (CEC 2016)
IEEE (2016), DOI: 10.1109/CEC2016.7743855

G.-J. de Vries, P. Lemmens, D. Brokken, S. Pauws, M. Biehl
Towards Emotion Classification Using Appraisal Modeling
In: Psychology and Mental Health - Concepts, Methodologies, Tools, and Applications.
IGI Global, Chapter 23, p. 552-572 (2016)
Reprinted version of : Int. J. of Synthetic Emotions 6(1): 40-59 (2016)

E. Mwebaze and M. Biehl
Prototype-based classification for image analysis and its application to crop disease diagnosis
In: E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.),
Advances in Self-Organizing Maps and Learning Vector Quantization
Proc. of the 11th International Workshop WSOM, 2016, Houston, Texas.
Springer Series on Adv. in Intelligent Systems and Computing, Vol. 428: pp. 329-339 (2016).
doi: 10.1007/978-3-319-28518-4_29

F. Melchert, U. Seiffert and M. Biehl
Functional Representation of Prototypes in LVQ and Relevance Learning
In: E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.),
Advances in Self-Organizing Maps and Learning Vector Quantization
Proc. of the 11th International Workshop WSOM, 2016, Houston, Texas.
Springer Series on Adv. in Intelligent Systems and Computing, Vol. 428: pp. 317-327 (2016).
doi: 10.1007/978-3-319-28518-4_28

D. Mudali, M. Biehl, K.L. Leenders and J.B.T.M. Roerdink
LVQ and SVM Classification of FDG-PET Brain Data
In: E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.),
Advances in Self-Organizing Maps and Learning Vector Quantization
Proc. of the 11th International Workshop WSOM, 2016, Houston, Texas.
Springer Series on Adv. in Intelligent Systems and Computing, Vol. 428: pp. 205-215 (2016).
doi: 10.1007/978-3-319-28518-4_18

M. Gay, M. Kaden, M. Biehl, T. Villmann and A. Lampe
Complex Variants of GLVQ Based on Wirtinger's Calculus
In: E. Merenyi, M.J. Mendenhall, P. O'Driscoll (eds.),
Advances in Self-Organizing Maps and Learning Vector Quantization
Proc. of the 11th International Workshop WSOM, 2016, Houston, Texas.
Springer Series on Adv. in Intelligent Systems and Computing, Vol. 428: pp. 293-303 (2016).
doi: 10.1007/978-3-319-28518-4_26

F. Melchert, M. Biehl and U. Seiffert
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)

2016 - Technical reports, abstracts, other publications

R. Smedinga, M. Biehl, F. Kramer (editors)
13 th SC@RUG 2015-2016, Proc. of the Student Colloquium Computer Science
University of Groningen, 60 pages, 2016.

F. Melchert, U. Seiffert, M. Biehl.
Functional Representation of Prototypes in LVQ and Relevance Learning
Proc. BNAIC 2016, Amsterdam, pp. 165-166, 2016.

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.
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

E. Baranowski, K. Bunte, C. Shackleton, A. Taylor, B. Hughes, M. Biehl, P. Tino, T. Guran, W. Arlt
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

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.
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

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.
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

O. de Wiljes, R.A. van Elburg, M. Biehl, F.A. Keijzer
Modeling spontaneous activity across an excitable epithelium: Support for a coordination scenario of early neural evolution
Front. Comput. Neurosci. (2015), available online (click on title)
doi:10.10.3389/fncom.2015.00110

Y. Leo, N. Adlard, M. Biehl, M. Juarez, T. Smallie, M. Snow, C.D. Buckley, K. Raza, A. Filer, D. Scheel-Toellner
Expression of chemokines CXCL4 and CXCL7 by synovial macrophages defines early stage of rheumatoid arthritis
Ann. of the Rheumatic Disease (online first) 2015
doi:10.1136/annrheumdis-2014-206921

I. Giotis, N. Molders, S. Land, M. Biehl, M.F. Jonkman, N. Petkov
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

J.J.G. de Vries, P.M.C. Lemmens, D. Brokken, S.C. Pauws, and M. Biehl
Towards Emotion Classification Using Appraisal Modeling
International Journal of Synthetic Emotions 6(1): 40-59 (2015)
doi:10.4018/IJSE.2015010103

J.J.G. de Vries, S.C. Pauws, and M. Biehl
Insightful Stress Detection from Physiology Modalities using Learning Vector Quantization
Neurocomputing 151 (2): 873-882 (2015)
doi:10.1016/j.neucom.2014.10.008

M. Lange, M. Biehl, T. Villmann
Non-Euclidean Principal Component Analysis by Hebbian Learning
Neurocomputing 147: 107-119 (2015)
doi:10.1016/j.neucom.2013.11.049

2015 - Conference contributions and book chapters

A. Schulz, B. Mokbel, M. Biehl, B. Hammer
Inferring Feature Relevances From Metric Learning
In: 2015 IEEE Symposium Series on Computational Intelligence, pp. 1599-1606 (2015)
doi: 10.1109/SSCI.2015.225

F. Melchert, U. Seiffert, M. Biehl
Polynomial Approximation of Spectral Data in LVQ and Relevance Learning
In: New Challenges in Neural Computation, Workshop at the GCPR, Aachen (2015),
Machine Learning Reports 03-2015: 25-32 (2015)

G.-J. de Vries, S. Pauws M. Biehl
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

T. Villmann, M. Kaden, D. Nebel and M. Biehl
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

M. Biehl, B. Hammer, F.-M. Schleif, P. Schneider, T. Villmann
Stationarity of Matrix Relevance LVQ
In:International Joint Conference on Neural Networks (IJCNN 2015),
doi:10.1109/IJCNN.2015.7280441

E. Mwebaze, G. Bearda, M. Biehl, D. Zühlke
Combining dissimilarity measures for prototype-based classification
In: M. Verleysen (editor), Proc. of the 23rd European Symposium on Artificial Neural Networks (ESANN) 2015
d-side publishing, 31-36 (2015)

2015 - Technical reports, abstracts, other publications

A. Taylor et al.
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

K. Lang et al.
Urine steroid metabolomics as a diagnostic tool in primary aldosteronism.
Endocrine Abstracts 38: OC1.6 (2015)
doi:10.1530/endoabs.38.OC1.6

V. Chortis et al.
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

M. Biehl, A. Ghio, F.-M. Schleif
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

R. Smedinga, M. Biehl, F. Kramer (editors)
12th SC@RUG 2014-2015, Proc. of the Student Colloquium Computer Science
University of Groningen, 74 pages, 2015.


Publications in 2014

2014 - Journal Articles

C.F. Davies et al. (link to list of authors and bibliographical information),
The somatic genomic landscape of chromophobe renal cell carcinoma
Cancer Cell 26 (3): 319-330 (2014)

E. Bilal, T. Sakellropoulos, Challenge Participants(*), I.N. Melas, D.E. Messinis, V. Belcastro, K. Rhrissorrakrai, P. Meyer,
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 in 2014) open access
doi:10.1093/bioinformatics/btu659

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
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

S. Hormoz, G. Bhanot, M. Biehl, E. Bilal, P. Meyer, R. Norel, K. Rhrissorrakrai, A. Dayarian
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

M. Biehl, P. Sadowski, G. Bhanot, E. Bilal, A. Dayarian, P. Meyer, R. Norel, K. Rhrissorrakrai, M.D. Zeller, S. Hormoz
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

2014 - Conference contributions and book chapters

H.T. Kruitbosch, I. Giotis, M. Biehl
Segmented shape-symbolic time series representation
In: M. Verleysen (editor), Proc. of the 22nd European Symposium on Artificial Neural Networks ESANN 2014, d-side publishing, pp. 259-264 (2014)

B. Frenay, D. Hofmann, A. Schulz, M. Biehl, B. Hammer
Valid Interpretation of Feature Relevance for Linear Data Mappings. accepted contribution to the IEEE
Symp. on Computational Intelligence, SSCI 2014. IEEE (2014)

M. Biehl, B. Hammer, T. Villmann
Distance measures for prototype based classification
Preprint version. Invited contribution, in: BrainComp, Proc. of the International Workshop on Brain-Inspired Computing, Cetraro/Italy, July 2013
L. Grandinetti et al. (eds.), Springer Lecture Notes Vol. 8603, pp. 100-116 (2014)

2014 - Technical reports, abstracts, other publications

M. Biehl, M. Kaden, T. Villmann
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)

R. Smedinga, M. Biehl, F. Kramer (editors)
11th SC@RUG 2013-2014, Proc. of the Student Colloquium Computer Science
University of Groningen, 72 pages, 2014.

M. Biehl
Prototype-based classifiers and their application in the life sciences (abstract).
In: 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.

M. Biehl, M. Kaden, P. Stürmer, T. Villmann.
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)

A. Schulz, D. Hofmann, M. Biehl, B. Hammer
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

I. Giotis, K. Bunte, N. Petkov, M. Biehl
Adaptive Matrices and Filters for Color Texture Classification
Journal of Mathematical Imaging and Vision 47: 79-92 (2013)
(Springer, open access since May 2012)
doi:10.1007/s10851-012-0356-9

E. Alegre, M. Biehl, N. Petkov, L. Sanchez
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

M. Biehl, K. Bunte, P. Schneider
Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization
PLOS One 8: e59401 (2013), (open access)
doi:10.1371/journal.pone.0059401

N. Aghaeepour, G. Finak, The FlowCAP Consortium, The DREAM Consortium (*), H. Hoos, T.R. Mosmann, R. Brinkman, R. Gottardo, and R.H. Scheuermann.
(* 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

2013 - Conference contributions and book chapters

M. Strickert, B. Hammer, T. Villmann, M. Biehl
Regularization and improved interpretation of linear data mappings and adaptive distance measures
Preprint version of a contribution to the 2013 IEEE Symp. on Computational Intelligence and Data Mining (CIDM)
In: Proc. IEEE SSCI 2013. Article available online (2013)

M. Lange, M. Biehl, T. Villmann
Non-Euclidean Independent Component Analysis and Oja's Learning
In: Proc. 21st Europ. Symp. Artificial Neural Networks (ESANN), M. Verleysen (ed.), d-side publishing, pp. 125-130 (2013)

M. Biehl, M. Kästner, M. Lange, T. Villmann
Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects
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 (2013), pp 23-33, (available online)


Publications in 2012

2012 - Journal Articles

M. Biehl
Admire LVQ - Adaptive Distance Measures in Relevance Learning Vector Quantization
KI - Künstliche Intelligenz 26: 391-395 (2012)
(Springer, open access)

M.B. Huber, K. Bunte, M.B. Nagarajan, M. Biehl, L.A. Ray, A. Wismüller
Texture Feature Ranking with Relevance Learning to Classify Interstitial Lung Disease Patterns
Artificial Intelligence in Medicine 56: 91-97 (2012)
(Elsevier, available online)

M. Kästner, B. Hammer, M. Biehl, T. Villmann
Functional Relevance Learning in Generalized Learning Vector Quantization
preprint version of: Neurocomputing 90: 85-95 (2012)

K. Bunte, S. Haase, M. Biehl, T. Villmann
Stochastic Neighbor Embedding (SNE) for Dimension Reduction and Visualization using arbitrary Divergences
preliminary version of: Neurocomputing 90: 23-45 (2012)

K. Bunte, P. Schneider, B. Hammer, F.-M. Schleif, T. Villmann, M. Biehl
Limited rank matrix learning - discriminative dimension reduction and visualization
preprint-version of: Neural Networks 26: 159-173 (2012)

K. Bunte, M. Biehl, B. Hammer
A general framework for dimensioniality reducing data visualization mapping
preprint version of: Neural Computation 24: 771-804 (2012)

2012 - Conference contributions and book chapters

M. Kästner, D. Nebel, M. Riedel, M. Biehl, T. Villmann
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

M. Biehl, K. Bunte, F.-M. Schleif, P. Schneider, T. Villmann
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

G. Peters, K. Bunte, M. Strickert, M. Biehl, T. Villmann
Visualization of processes in self-learning systems
Proc. Tenth Annual Conf. Privacy, Security, and Trust, Paris 2012, pp. 244-249 (2012)
IEEE, available online

B. Mokbel, W. Lueks, A. Gisbrecht, M. Biehl, B. Hammer
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)

K. Bunte, F.-M. Schleif, M. Biehl
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)

M. Biehl, P. Schneider, D. Smith, H. Stiekema, A. Taylor, B. Hughes, C. Shackleton, P. Stewart, W. Arlt
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)

2012 - Other Publications, Abstracts, Technical Reports, etc.

R. Smedinga, M. Biehl, F. Kramer (editors)
9th SC@RUG 2011-2012, Proc. of the Student Colloquium Computer Science
University of Groningen, 57 pages, 2012.


Publications in 2011

2011 - Journal Articles

W. Arlt, M. Biehl, A.E. Taylor, S. Hahner, R. Libe, B.A. Hughes, P. Schneider, D.J. Smith, H. Stiekema, N. Krone,
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 96: 3775-3784 (2011).
available online

K. Bunte, M. Biehl, M.F. Jonkman, N. Petkov
Learning Effective Color Features for Content Based Image Retrieval in Dermatology
Pattern Recognition 44 (2011) 1892-1902.
available online at Science Direct.

K. Bunte, B. Hammer, T. Villmann, M. Biehl, A. Wismüller
Neighbor Embedding XOM for Dimension Reduction and Visualization
Neurocomputing, 74 (2011) 1340-1350.

E. Mwebaze, P. Schneider, F.-M. Schleif, J.R. Aduwo, J.A. Quinn, S. Haase, T. Villmann, M. Biehl
Divergence based classification in Learning Vector Quantization
preprint version of: Neurocomputing, 74 (2011) 1429-1435.

2011 - Conference proceedings and book chapters

K. Bunte, I. Giotis, N. Petkov, M. Biehl
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.),
Computer Analysis of Images and Patterns - 14th International Conference, CAIP 2011, Seville, Spain, August 2011, Proc. Part II,
Springer LNCS Vol.6855
, 489-497 (2011).

B. Hammer, M. Biehl, K. Bunte, B. Mokbel
A general framework for dimensionality reduction for large data sets
In: Advances in Self-Organizing Maps, 8th Workshop on Self-Organizing Maps WSOM 2011,
J. Laaksonen, T. Honkela (eds.), Springer LNCS Vol. 6731, 277-287 (2011).

J. Quinn, J. Mooij, T. Heskes, M. Biehl
Learning of Causal Relations
tutorial paper for a special session at ESANN 2011, in: Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 287-296 (2011)

P. Schneider, T. Geweniger, F.-M. Schleif, M. Biehl, T. Villmann
Multivariate class labeling in Robust Soft LVQ
preprint version of: Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 17-22 (2011)

E. Mwebaze, J. Quinn, M. Biehl
Causal Relevance learning for robust classification under interventions
preprint version of: 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 315-320 (2011)

M. Kästner, B. Hammer, M. Biehl, T. Villmann
Generalized Functional Relevance Learning Vector Quantization
preprint version of: 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 93-98 (2011)

K. Bunte, M. Biehl, B. Hammer
Supervised dimension reduction mappings
preprint version of: Proc. 19th Europ. Symp. on Artificial Neural Networks,
M. Verleysen (ed.), d-side publishing, 281-286 (2011)

K. Bunte, M. Biehl, B. Hammer
Dimensionality reduction maps
preprint version of an article in: Proc. IEEE Symp. on Computational
Intelligence and Data Mining SSCI 2011 CDIM, Paris, April 2011. Pages 349-356 (2011)

M.B. Huber, K. Bunte, M.B. Nagajaran, M. Biehl, L.A. Ray, A. Wismüller
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 (2011)

2011 - Other Publications, Abstracts, Technical Reports, etc.

G. Papari, K. Bunte, M. Biehl
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,

W. Lueks, B. Mokbel, M. Biehl, B. Hammer
How to evaluate Dimensionality Reduction?
Technical Report, in: B. Hammer and T. Villmann (eds.),
Proc. Workshop New Challenges in Neural Computation 2011, pp. 29-37, available online,

R. Smedinga, M. Biehl, F. Kramer (editors)
8th SC@RUG 2010-2011, Proc. of the Student Colloquium Computer Science
University of Groningen, 123 pages, 2011.

M. Kästner, T. Villmann, M. Biehl
About Sparsity in Functional Relevance Learning in Generalized Learning Vector Quantization
Technical Report, Machine Learning Reports, 03/2011, pdf available online, 2011


Publications in 2010

2010 - Journal Articles

A. Witoelar, A. Ghosh, J.J.G. de Vries, B. Hammer, M. Biehl
Window-based example selection in Learning Vector Quantization
preprint version of: Neural Computation 22: 2924-2961 (2010)

A. Offringa, G. de Bruyn, M. Biehl, S. Zaroubi, G. Bernardi, V. Pandey
Post-correlation radio frequency interference classification methods
Monthly Notices of the Royal Astronomical Society 405: 155-167 (2010)

P. Schneider, K. Bunte, H. Stiekema, B. Hammer, T. Villmann, M. Biehl
Regularization in Matrix Relevance Learning
preprint version of: IEEE Trans. Neural Networks 21: 831-840 (2010)
online: IEEE Xplore

K. Bunte, B. Hammer, A. Wismüller, M. Biehl
Adaptive Local Dissimilarity measures for Discriminative Dimension Reduction of Labeled Data
preprint version of: Neurocomputing 73: 1074-1092 (2010).

P. Schneider, M. Biehl, B. Hammer
Hyperparameter Learning in Probabilistic Prototype-Based Models
preprint version of: Neurocomputing 73: 1117-1124 (2010)

2010 - Conference proceedings and book chapters

A. Offringa, A.G. de Bruyn, S. Zaroubi, M. Biehl
The LOFAR RFI detection pipeline and its first results

RFI mitigation workshop RFI2010, Groningen, March 2010, to be published in Proceedings of Science (2010)

F.-M. Schleif, T. Villmann, B. Hammer, P. Schneider, M. Biehl
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)

E. Mwebaze, P. Schneider, F.-M. Schleif, S. Haase, T. Villmann, M. Biehl
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)

K. Bunte, B. Hammer, T. Villmann, M. Biehl, A. Wismüller
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).

T. Villmann, S. Haase, F.-M. Schleif, B. Hammer, M. Biehl
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).

2010 - Other Publications, Abstracts, Technical Reports, etc.

R. Smedinga, M. Biehl, F. Kramer (editors)
7th SC@RUG 2009-2010, Proc. of the Student Colloquium Computer Science
University of Groningen, 46 pages, 2010.

B. Hammer, K. Bunte, M. Biehl
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

O. de Wijles, R.A.J. van Elburg, M. Biehl, F. Keijzer
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.

A. Taylor, M. Biehl, B. Hughes, H. Stiekema, P. Schneider, D. Smith, P. Nightingale, C. Shackleton, P. Stewart, W. Arlt
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)


Publications in 2009

2009 - Edited volumes and issues

M. Biehl, B. Hammer, M. Verleysen, T. Villmann (eds.)
Similiarity based clustering
Springer Lecture Notes Artificial Intelligence Vol. 5400/2009

F.-M. Schleif, M. Biehl, A. Vellido,
Andvances in machine learning and computational intelligence
Special issue: Neurocomputing 72, Editorial: pages 1377-1378 (2009)

2009 - Journal Articles

P. Schneider, M. Biehl, B. Hammer,
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)

P. Schneider, M. Biehl, B. Hammer
Distance learning in discriminative vector quantization
preprint version of: Neural Computation 21, 2942-2969 (2009)
journal article available on-line at MIT Press (2009)

A. Witoelar, M. Biehl
Phase transitions in vector quantization and neural gas
preprint version of: Neurocomputing 72, 1390-1397 (2009)

2009 - Conference proceedings and book chapters

M. Biehl, B. Hammer, P. Schneider, T. Villmann
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)

T. Villmann, B. Hammer, M. Biehl
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)

M. Biehl, N. Caticha, P. Riegler
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)

K. Bunte, B. Hammer, M. Biehl
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)

M. Strickert, J. Keilwagen, F.-M. Schleif, T. Villmann, M. Biehl
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)

P. Schneider, M. Biehl, B. Hammer
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)

A. Witoelar, M. Biehl, B. Hammer
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)

K. Bunte, M. Biehl, N. Petkov, M.F. Jonkman
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)

K. Bunte, B. Hammer, P. Schneider, M. Biehl
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)

2009 - Abstracts, Technical Reports

W. Arlt, S. Hahner, R. Libe, B.A. Hughes, M. Biehl, H. Stiekema, P. Schneider, et al.,
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)

T. Geweniger, P. Schneider, F.-M. Schleif, M. Biehl, T. Villmann
Extending RSLVQ to handle data points with uncertain class assignments
Machine Learning Reports 02/2009, Univ. Leipzig (2009)

M. Biehl, B. Hammer, F.-M. Schleif, P. Schneider, T. Villmann
Stationarity of Matrix Relevance Learning Vector Quantization
Machine Learning Reports 01/2009, Univ. Leipzig (2009)

M. Biehl, B. Hammer, S. Hochreiter, S.C. Kremer, T. Villmann
Similarity-based learning on structures
Summary and abstract collection, Dagstuhl Seminar Proceedings 09081, 2009.


Publications in 2008

2008 - Journal Articles

N. Petkov, E. Alegre, M. Biehl, and L. Sanchez
Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ

Computers in Biology and Medicine 38: 461-468 (2008)

A. Witoelar, M. Biehl, A. Ghosh, B. Hammer,
Learning Dynamics of Neural Gas and Vector Quantization
preprint version of Neurocomputing 71: 1210-1219 (2008)

S. Weber, M. Biehl, M. Kotrla, W. Kinzel
Simulation of self-assembled nanopatterns in strained 2D alloys on the fcc(111) surface
J. Phys.: Cond. Matter 20: 265004 (2008)
preprint version: (PDF)

2008 - Conference contributions

M. Strickert, P. Schneider, J. Keilwagen, T. Villmann, M. Biehl, B. Hammer
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)

M. Strickert, K. Witzel, J. Keilwagen, H.P. Mock, P. Schneider, M. Biehl, T. Villmann
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)

P. Schneider, F.-M. Schleif, T. Villmann, M. Biehl
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.

A. Witoelar, A. Ghosh, M. Biehl
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.

2008 - Abstracts, Technical Reports, Editorials

P. Schneider, K. Bunte, H. Stiekema, B. Hammer, T. Villmann, M. Biehl
Regularization in Matrix Relevance Learning
Machine Learning Reports, Report 02/2008, Univ. Leipzig

K. Bunte, P. Schneider, B. Hammer, F.-M. Schleif, T. Villmann, M. Biehl
Discriminative Visualization by Limited Rank Matrix Learning
Machine Learning Reports, Report 03/2008, Univ. Leipzig

P. Schneider, M. Biehl, B. Hammer
Matrix adaptation in discriminative vector quantization
IfI Technical Report Series, IFI-08-08, TU Clausthal (2008)

A. Witoelar, M. Biehl
Equilibrium physics approach in vector quantization
Part I: General formalism and high temperature limit
Part II: Annealed approximation (preliminary version)

F. Rossi, C. Angulo Bahon, M. Biehl
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


Publications in 2007

2007 - Journal Articles

M. Biehl, A. Ghosh, and B. Hammer
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

M. Walther, M. Biehl, W. Kinzel
Formation and consequences of misfit dislocations in heteroepitaxial growth
Physica Status Solidi (C) 4:3210-3220, 2007
preprint version: (PDF)

2007 - Conference Contributions

M. Biehl, R. Breitling, Y. Li,
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)

A. Witoelar, M. Biehl, and B. Hammer
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)

P. Schneider, M. Biehl, F.-M. Schleif, B. Hammer
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)

M. Kotrla, S. Weber, F. Much, M. Biehl, W. Kinzel
Self-organised nano-patterns in strained 2D metallic alloys: droplets vs. stripes
Acta Metallurgica Slovaca 13:70-75, 2007

P. Schneider, M. Biehl, and B. Hammer
Relevance Matrices in Learning Vector Quantization
preprint version of a paper published in Proc. 15th Europ. Symp. on Artificial Neural Networks, ESANN 2007, M. Verleysen (ed.), d-side publishing (2007) pg. 37

A. Witoelar, M. Biehl, A. Ghosh, and B. Hammer
On the Dynamics of Vector Quantization and Neural Gas
preprint version of a paper published in Proc. 15th Europ. Symp. on Artificial Neural Networks, ESANN 2007, M. Verleysen (ed.), d-side publishing (2007) pg. 127

N. Petkov, E. Alegre, M. Biehl, and L. Sanchez
LVQ acrosome integrity assessment of boar sperm cells
preprint version of a paper in: Proc. CompImage 2006, Coimbra/Portugal, eds. J.M.R.S. Taveres and R.M.N. Jorge, Taylor and Francis, 2007.

2007 - Abstracts, Technical Reports, Editorials

M. Biehl, E. Merenyi, F. Rossi,
Advances in computational intelligence and learning
(link to the journal page),
Editorial of a special issue, Neurocomputing 70: 1117-1119, 2007

M. Biehl, B. Hammer, M.Verleysen, T. Villmann
Similarity-based clustering and its application to medicine and biology
Summary and abstract collection, Dagstuhl Seminar Proceedings 07131, 2008.


Publications in 2006

2006 - Journal Articles

A. Ghosh, M. Biehl, and B. Hammer
Performance analysis of LVQ algorithms: a statistical physics approach
preprint version of an article in: Neural Networks 19: 817 (2006)

M. Biehl, A. Ghosh, and B. Hammer
Learning Vector Quantization: the dynamics of Winner-Takes-All algorithms
preprint version of an article in: Neurocomputing 69 (7-9): 660-670 (2006)

2006 - Conference Contributions

M. Biehl, P. Pasma, M. Pijl, L. Sanchez, and N. Petkov
Classification of Boar Sperm Head Images using Learning Vector Quantization
preprint version of a paper in:
ESANN 2006, Proc. European Symposium on Artificial Neural Networks
in Bruges/Belgium, April 2006
M. Verleysen (ed.), d-side publishing, 2006.

2006 - Technical Reports

M. Biehl, B. Hammer, and P. Schneider
Matrix Learning in Learning Vector Quantization
Ifl Technical Report Series, Ifl-06-14, TU Clausthal, 2006.


Publications in 2005

2005 - Journal Articles

T. Volkmann, M. Ahr, and M. Biehl
Kinetic model of II-VI(001) semiconductor surfaces: growth rates in Atomic Layer Epitaxy
preprint version of an article in Phys. Rev. B 69 (2004) 165303
(PS version)    (PDF version)

T. Volkmann, F. Much, M. Biehl, M. Kotrla
Interplay of strain relaxation and chemically induced diffusion barriers: nanostructure formation in 2D alloys
Surface Science 586 (2005) 157-173
preprint version available at the cond-mat archive: (direct link to the paper)

2005 - Conference Contributions

A. Ghosh, M. Biehl, and B. Hammer
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.

C. Bunzmann, M. Biehl, and R. Urbanczik
Efficient training of multilayer perceptrons using principal component analysis
preprint version of Phys. Rev. E 72: 026117 (2005)

M. Biehl, A. Ghosh, and B. Hammer
The dynamics of Learning Vector Quantization

preprint version of a paper in:
ESANN 2005, Proc. European Symposium on Artificial Neural Networks
in Bruges/Belgium, April 2005
M. Verleysen (ed.), d-side publishing (2005), 13-18

M. Biehl
Lattice gas models and Kinetic Monte Carlo simulations of epitaxial growth
preprint version of an invited contribution to an MFO Mini-Workshop (Oberwolfach, 2004),
in: Multiscale Modeling in Epitaxial Growth,
ed. A. Voigt, Int. Series of Numerical Mathematics 149 (Birkhaeuser, 2005), 3-18
(PS)    (PDF)

M. Biehl, F. Much, and C. Vey
Off-lattice Kinetic Monte Carlo simulations of strained heteroepitaxial growth
preprint version of an invited contribution to an MFO Mini-Workshop (Oberwolfach, 2004),
in: Multiscale Modeling in Epitaxial Growth,
ed. A. Voigt, Int. Series of Numerical Mathematics 149 (Birkhaeuser, 2005), 41-57
(PS)    (PDF)

M. Biehl and F. Much
Off-lattice Kinetic Monte Carlo simulations of Stranski-Krastanov-like growth
preprint version of an invited contribution to the NATO-ARW on
Quantum Dots: Fundamentals, Applications, and Frontiers, June 2003,
eds. B. Joyce, P. Kelires, A. Naumovets, and D.D. Vvedensky,
NATO Sciences Series II: Mathematics, Physics, and Chemistry Vol. 190, Springer (2005)
(PS version)    (PDF version)


Publications in 2004

A. Ghosh, M. Biehl, A. Freking, G. Reents
A theoretical framework for analyzing the dynamics of learning vector quantization
Technical Report 2004-9-02, Mathematics and Computing Science, University Groningen, 2004.

T. Volkmann, M. Ahr, and M. Biehl
Kinetic model of II-VI(001) semiconductor surfaces: growth rates in Atomic Layer Epitaxy
preprint version of an article in Phys. Rev. B 69 (2004) 165303
(PS version)    (PDF version)


For publications before 2004, click here.