Componentwise support vector machines for structure detection

Authors:
K. Pelckmans;J. A. K. Suykens;B. De Moor
Affiliations:
K.U.Leuven ESAT-SCD/SISTA, Leuven, Belgium;K.U.Leuven ESAT-SCD/SISTA, Leuven, Belgium;K.U.Leuven ESAT-SCD/SISTA, Leuven, Belgium
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 6
Cited 1

Least Squares Support Vector Machine Classifiers

Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Structural Modelling with Sparse Kernels

Machine Learning
Concept acquisition through representational adjustment

Concept acquisition through representational adjustment
Convex Optimization

Convex Optimization
Building sparse representations and structure determination on LS-SVM substrates

Neurocomputing

Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper extends recent advances in Support Vector Machines and kernel machines in estimating additive models for classification from observed multivariate input/output data. Specifically, we address the question how to obtain predictive models which gives insight into the structure of the dataset. This contribution extends the framework of structure detection as introduced in recent publications by the authors towards estimation of componentwise Support Vector Machines (cSVMs). The result is applied to a benchmark classification task where the input variables all take binary values.