Publications
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Integrated inference and learning of neural factors in structural support vector machines Ghent University
A practical approach to model selection for support vector machines with a gaussian kernel Ghent University
Extraction from support vector machines University of Antwerp
Recurrent sparse support vector regression machines trained by active learning in the time-domain KU Leuven
A method for the sparse solution of recurrent support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity and allows the user to adjust the complexity of the resulting model. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on the accuracy of the fully recurrent model using ...
Robust support vector machines for classification with nonconvex and smooth losses KU Leuven
This letter addresses the robustness problem when learning a large margin classifier in the presence of label noise. In our study, we achieve this purpose by proposing robustified large margin support vector machines. The robustness of the proposed robust support vector classifiers (RSVC), which is interpreted from a weighted viewpoint in this work, is due to the use of nonconvex classification losses. Besides the robustness, we also show that ...
Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso KU Leuven
© Springer International Publishing AG 2017. Performing predictions using a non-linear support vector machine (SVM) can be too expensive in some large-scale scenarios. In the non-linear case, the complexity of storing and using the classifier is determined by the number of support vectors, which is often a significant fraction of the training data. This is a major limitation in applications where the model needs to be evaluated many times to ...
Use of Support Vector Machines Approach via ComBat Harmonized Diffusion Tensor Imaging for the Diagnosis and Prognosis of Mild Traumatic Brain Injury: A CENTER-TBI Study Interuniversity Microelectronics Centre University of Antwerp
The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging. Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the variance in outcome, as many patients with incomplete recovery will have normal-appearing clinical neuroimaging. More advanced quantitative techniques such as diffusion MRI (dMRI), can detect microstructural changes not otherwise visible, and so may offer a way to ...
T-wave end detection using neural networkss and support vector machines KU Leuven
BACKGROUND AND OBJECTIVE: In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. METHODS: Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random ...
Multi-View Least Squares Support Vector Machines Classification Vrije Universiteit Brussel KU Leuven
In multi-view learning, data is described using different representations, or views. Multi-view classification methods try to exploit information from all views to improve the classification performance. Here a new model is proposed that performs classification when two or more views are available. The model is called Multi-View Least Squares Support Vector Machines (MV-LSSVM) Classification and is based on solving a constrained optimization ...