28 Result(s)
within
Conference Paper
Bart De Moor
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Chapter and Conference Paper
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may deteriorate in less dense region...
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Chapter and Conference Paper
The Bures Metric for Generative Adversarial Networks
Generative Adversarial Networks (GANs) are performant generative methods yielding high-quality samples. However, under certain circumstances, the training of GANs can lead to mode collapse or mode drop**. To...
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Chapter and Conference Paper
A Genetic Algorithm for Pancreatic Cancer Diagnosis
Pancreatic cancer is one of the leading causes of cancer-related death in the industrialized countries and it has the least favorable prognosis among various cancer types. In this study we aim to facilitate ea...
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Chapter and Conference Paper
A Hybrid Approach to Feature Ranking for Microarray Data Classification
We present a novel approach to multivariate feature ranking in context of microarray data classification that employs a simple genetic algorithm in conjunction with Random forest feature importance measures. W...
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Chapter and Conference Paper
Towards Better Prioritization of Epigenetically Modified DNA Regions
Epigenetic modifications of the genome can cause profound changes in phenotype of an organism. Experimental methods allow us to detect regions of the DNA that have been epigenetically modified; these regions a...
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Chapter and Conference Paper
A Simple Genetic Algorithm for Biomarker Mining
We present a method for prognostics biomarker mining based on a genetic algorithm with a novel fitness function and a bagging-like model averaging scheme. We demonstrate it on publicly available data sets of g...
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Chapter and Conference Paper
Semi-supervised Learning of Sparse Linear Models in Mass Spectral Imaging
We present an approach to learn predictive models and perform variable selection by incorporating structural information from Mass Spectral Imaging (MSI) data. We explore the use of a smooth quadratic penalty ...
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Chapter and Conference Paper
Hybrid Clustering of Multiple Information Sources via HOSVD
We present a hybrid clustering algorithm of multiple information sources via tensor decomposition, which can be regarded an extension of the spectral clustering based on modularity maximization. This hybrid cl...
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Chapter and Conference Paper
Identifying Customer Profiles in Power Load Time Series Using Spectral Clustering
An application of multiway spectral clustering with out-of-sample extensions towards clustering time series is presented. The data correspond to power load time series acquired from substations in the Belgian ...
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Chapter and Conference Paper
Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes
It has been shown that Kernel Based Regression (KBR) with a least squares loss has some undesirable properties from robustness point of view. KBR with more robust loss functions, e.g. Huber or logistic losses,...
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Chapter and Conference Paper
Classification of Sporadic and BRCA1 Ovarian Cancer Based on a Genome-Wide Study of Copy Number Variations
Motivation: Although studies have shown that genetic alterations are causally involved in numerous human diseases, still not much is known about the molecular mechanisms involved in sporadic and hereditary ovaria...
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Chapter and Conference Paper
Ontology Guided Data Integration for Computational Prioritization of Disease Genes
In this paper we present our progress on a framework for collection and presentation of biomedical information through ontology-based mediation. The framework is built on top of a methodology for computational...
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Chapter and Conference Paper
KSinBIT 2006 PC Co-chairs’ Message
The impact of the upcoming Internet on scientific research worldwide was enormous, not the least in biomedical research. Especially the Human Genome Project was the inspiration for many biological databases pu...
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Chapter and Conference Paper
Interpreting Gene Profiles from Biomedical Literature Mining with Self Organizing Maps
We present an approach to interpret gene profiles derived from biomedical literature using Self Organizing Maps (SOMs). Comparison of different clustering algorithms shows that SOMs perform better in grou** ...
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Chapter and Conference Paper
Load Forecasting Using Fixed-Size Least Squares Support Vector Machines
Based on the Nyström approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large scale regression problem. This is...
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Chapter and Conference Paper
Learning from General Label Constraints
Most machine learning algorithms are designed either for supervised or for unsupervised learning, notably classification and clustering. Practical problems in bioinformatics and in vision however show that thi...
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Chapter and Conference Paper
Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs
This paper contrasts three related regularization schemes for kernel machines using a least squares criterion, namely Tikhonov and Ivanov regularization and Morozov’s discrepancy principle. We derive the condi...
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Chapter and Conference Paper
Compactly Supported RBF Kernels for Sparsifying the Gram Matrix in LS-SVM Regression Models
In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels, recently proposed by Genton, may lead to computational imp...
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Chapter and Conference Paper
Extended Bayesian Regression Models: A Symbiotic Application of Belief Networks and Multilayer Perceptrons for the Classification of Ovarian Tumors
We describe a methodology based on a dual Belief Network-Multilayer Perceptron representation to build Bayesian classifiers. This methodology combines efficiently the prior domain knowledge and statistical dat...
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Chapter and Conference Paper
Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines
A key idea of nonlinear Support Vector Machines (SVMs) is to map the inputs in a nonlinear way to a high dimensional feature space, while Mercer’s condition is applied in order to avoid an explicit expression ...
