Componentwise Least Squares Support Vector Machines

Simple item page
dc.creatorPelckmans, Kristiaan
dc.creatorGoethals, Ivan
dc.creatorDe Brabanter, Jos
dc.creatorSuykens, Johan A. K.
dc.creatorDe Moor, Bart
dc.date2005-04-19
dc.date.accessioned2026-07-25T16:53:43Z
dc.descriptionThis chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classification as well as the regression case. Furthermore, different techniques are proposed to discover structure in the data by looking for sparse components in the model based on dedicated regularization schemes on the one hand and fusion of the componentwise LS-SVMs training with a validation criterion on the other hand. (keywords: LS-SVMs, additive models, regularization, structure detection)
dc.description22 pages. Accepted for publication in Support Vector Machines: Theory and Applications, ed. L. Wang, 2005
dc.identifierhttps://arxiv.org/abs/cs/0504086
dc.identifierhttp://arxiv.org/abs/cs/0504086
dc.identifier.urihttps://dspace.dare.co.zw/handle/123456789/43939
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.subjectI.2.6
dc.titleComponentwise Least Squares Support Vector Machines
dc.typetext

Files

Collections

arXiv