Please visit the corresponding Brightspace pages for BSc, internships, and MSc project for further information on the formal requirements and organization.
Students from other programs (e.g. AI): be aware that specific rules may
apply and that you will have to
find a first supervisor within your program.
Rogchert Zijlstra (2022)
Feature relevance bounds in classification problems
Neha Bari Tamboli (2021)
Relevance matrix conditioning in GMLVQ
Pieter Jan Eilers (2021)
Analysis of Feedback Alignment
Ludger Visser (2021)
Filtering noise logs in ASML lithography machines
Ariadna Albors Zumel (2021)
Analysis of non-targeted metabolomics data using GMLVQ
Sofie Lövdal (2021, main supervisor: Amina Helmi, Kapteyn Institute)
Finding
structures in integral of motion space of the Milky Way halo
and proving their statistical significance
Anamitra Majumdar (2020)
Multi-class
Machine Learning Analysis of Steroid Metabolomics Data
Elisa Oostwal (2020)
Phase
transitions in layered neural networks: the role of the
activation function
Hayo Ottens (2020, ext. supervisor: Jelle
van Wezel, VerifAI)
A
comparison of supervised text detection methods applied to the visual
inspection zone of identity documents
Anton Laukemper (2020, joint supervision with
Herbert Jäger)
Classifying political YouTube channels with semi-supervised learning
Yuanqing Zhang (2020)
Supervised
Analysis of Steroid Metabolimics Data
Lars Doorenbos (2020, ext. supervision:
Erzsebet Merenyi, Rice Univ., Houston/TX)
Classification
of remotely sensed imagery for assessing machine learning algorithms
Gert-Jan van Ginkel (2020, ext. supervision:
Jelle van Wezel, VerfAI)
DLCNet:
Simultaneous Localization and Classification of Identity Documents
Emilio Oldenziel (2019, ext. supervision:
Sascha Saralajew, Porsche AG)
Provident detection of oncoming vehicles by night
Zhuoyun Kan (2019)
Lifelong Machine Learning Under Concept Drift - Enforcing Forgetting by Noise Accumulation
Michael LeKander (2019, main supervision: Marco Wiering)
Episodic control with drift compensation
Michiel Straat (2018)
Online learning in neural networks with ReLU activations
Jasper de Boer (2018)
The urinary steroid profile as a novel non-invasive alternative to stage non-alcoholic fatty liver disease
Bastiaan van Loon (2018, ext. supervision: R. Bosgraaf, ValueaA)
Fraud Detection in Energy Delivery: Using Hankel Matrices in Classification
Jelle van Wezel (2018, ext. supervision: R. Bosgraaf, ValueaA)
Voltage Predictions in Buried Gas Pipelines
Erik Jager (2018)
Explorative study on suitable classifiers for predicting a similarity score using a small set of similar websites
Antonious Koutounidis (2018), dayly supervision: Pierre Pinson,
Danish Technical University
Short-term wind capacity forecasts
based on measurements all over Europe
David Pavlov (2018)
Primary
aldosteronism analysis by means of machine learning
Thomas Nijman (2017)
Detection
of adrenocortical carcinoma recurrence using 24-hour urine samples and machine learning
Lidia Talavera Martinez (2017, Universitat Politecnica de Catalunya)
Identification of Parkinson's and Alzheimer's diseases
through the analysis of FDG-PET images using machine learning techniques
Martien Scheepens (2017)
GPLVQ-Prototype
Based Probabilistic Classification
Niels Kluiter (2017)
Efficient
reliability estimation of classifier systems
Rick van Veen (2016), daily supervisor: G.J. de Vries
Analysis
of Missing Data Imputation Applied to Heart Failure Data
Sreejita Ghosh (Biomedical Engineering 2016), daily supervision: G.J. de Vries,
co-supervisor: R. Renken
Heart failure severity prediction from medical records
Demy Idema (Life Sciences MSc Essay, 2017),
primary supervision: E. Hak, P. Horvatovich
Using prescription sequence symmetry analysis as a data-mining
pharmacoepidemiology: Identifying adverse-/protective effects
of ACE-inhibitors
Demy Idema (Life Sciences Research Report 1, 2016),
primary supervision: E. Hak, P. Horvatovich
The prescription sequence symmetry analysis (PSSA) study design in pharmacoepidemiology: a systematic review and simulation study
Harm de Vries (2014)
On optimization
of Generalized Matrix Learning Vector Quantization
Sander Kelders (2013)
LaMaLVQ: Warping Space
Eamon Nerbonne (2012)
Semi-localized
Matrix LVQ
Gjalt Bearda (2011)
Combining
distance measures in Learning Vector Quantization
Amirhosein Shantia (2011) (daily supervisor: Marco Wiering)
Automatic
Robot Learning Using Reinforcement Learning
Hedde Bosman (2010) (daily supervisor: Tjeerd Andringa)
Relating
head-movement to sound events
Moses Matovu (2010)
Adaptive
feature space transformation in Generalized Matrix Learning Vector Quantization
Han Stiekema (2009)
Tijmen Rozeboom (2009)
Ando Emerencia (2009)
Caesar Ogole (2008)
Wouter Storteboom (2008)
Gert-Jan de Vries (2007)
Marten Pijl (2006) (daily supervisor: R. Breitling)
G. van Schie (2004) (first supervisor: G. Renardel)
Alpheaus Feltham (2022) Rogchert Zijlstra (2021) Roland Veen (2021) Neha Bari Tamboli (2020) Tanja de Vries (2020) Abhishek Patil (2020) Pieter Jan Eilers (2020) Daan Opheikens (2020) Caroline Braams (2020, ext. supervision: Jelle van Wezel, VerifAI) Ariadna Albors Zumel (2020) Ludger Visser (2020) Anamitra Majumdar (2020) Elisa Oostwal (2020) Lars Doorenbos (2019, main supervision: Giuseppe Longo, Naples/Italy) Lars Holdijk (2019) Yuying Chen (2018) Zhuoyun Kan (2018) Amey Bhole (2018, main supervision: George Azzopardi) Jasper de Boer (2018, joint supervision: Katja Taxis) Yuying Chen (2018) Zhuoyun Kan (2018) David Pavlov (2018) Tim van Elteren (2018, main supervision: Michael Wilkinson) Antonios Koutounidis (2018) Jelle van Wezel (2018)
Thomas Heerdink (2023)
Kaitlin Vos (2023)
Lonneke Pulles (2021)
Floris Westerman (2019)
Joseph Jones (2018)
Lars Holdijk (2018)
Elisa Oostwal (2018), BSc Physics and Computer Science,
joint supervision with L.J.A. Koster
Sofie Lovdal (2018), primary supervision: N. Strisciuglio
Zahra Putrii Fitrianti (2018), primary supervision: N. Strisciuglio
Peri Rahamim (2018),jointly supervised with A. Meijster
Emilio Oldenziel (2017)
Harm de Vries and Mark Scheeve (2011)
Gjalt Bearda (2010), daily supervision: L.W. Wansbeek
Tumor Classification based on metabolomics data
Relevance LVQ for face detection
Student's t distributions in Learning Vector Quantization
k-Nearest
Neighbor and Learning Vector Quantization Relevance Learning via Semi-definite Programming
Simulation of Windowed LVQ algorithms
Analysis
of Robust Soft Learning Vector Quantization
Exploring metabolic space using machine learning technologies
Using
Support Vector Machines to solve large-scale and complex real world text categorization problems
back
Supervised MSc research internships (completed, selected recent examples)
- as first supervisor at the Bernoulli Institute
(if not specified otherwise)
- year of completion and link to archive or pdf if available
- note: access to many reports is restricted because the analysed
data is not public
Steroid
Metabolomics Data Analysis: Cushing's Syndrome
Constrained Relevance Matrix in Generalized Matrix Learning Vector Quantization
Direct
optimization of ROC-AUC
Order
parameter based study of hidden unit specialization in neural
networks for ReLU and sigmoidal activation functions
Improving
a neural network to classify movement disorders
Galaxy
classification of GAMA catalogue data using L-GMLVQ
On-line Learning Under Concept Drift
Matrix
Capsules with EM Routing compared with Convolutional Neural Networks
Image Binarization Techniques for Improving OCR Performance for Travel Documents
Exploration of non-targeted metabolomics data using unsupervised methods
Exploration of non-targeted metabolomics data by supervised learning
Constrained Relevance Matrices in Learning Vector Quantization
Learning
of single-layer neural networks: ReLU vs. sigmoidal activation
Comparison of Outlier Detection Methods on SDSS Image Datah
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacksh
Learning Vector Quantization for Ordinal Classification: a-OGMLVQ
Learning Vector Quantization for Ordinal Classification: p-OGMLVQ
Automatic identification of Holstein cattle using non-invasive computer vision approach
A post hoc analysis on two Multidisciplinary Multistep Medication Review effectiveness studies, using machine learning
Learning Vector Quantization for Ordinal Classification: a-OGMLVQ
Learning Vector Quantization for Ordinal Classification: p-OGMLVQ
Classification of non-alcoholic fatty liver disease (NAFLD)
A comparative study of human engineered features and learned features in deep convolutional neural networks for image classification
Analysis of gene expression data of tumor vs. normal cells in clear cell Renal Cell Carcinoma
Relevance Preprocessing for Generalized Learning Vector Quantization Methods
Supervised BSc thesis projects (completed)
- as first supervisor at the (J)BI if not specified otherwise
- year of completion and link to archive or pdf if available
- note: access to some theses may be restricted because the
analysed data is not public
Automatic detection of myoclonus bursts in EMG data
Iterative Subspace Correction Procedure for Generalized Matrix Learning Vector Quantization in Biomedicine
Probabilistic Matrix Relevance Learning Vector Quantization
Finding a distance metric for classification and outlier detection with supervised learning
Classification of Delirium Subtypes and Mortality Rates
Time
series classification using Hankel matrix based dissimilarity measures in Learning Vector Quantization
Efficiency
of organic solar cells: Improving a model for the fill factor
Efficient
implementation of the COSFIRE algorithm with CUDA
A Method
for Automatic Learning of COSFIRE Parameters
Is it possible to predict basketball results using statistical data?
Multi-Class
Classification of Functional Data
Batch
optimization methods in Generalized Matrix Relevance Learning
Vector Quantiation
Simulations
of the Zeeman Slower for the Al^41catraz experiment