Computer Science – Learning
Scientific paper
2005-04-19
Computer Science
Learning
22 pages. Accepted for publication in Support Vector Machines: Theory and Applications, ed. L. Wang, 2005
Scientific paper
This 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)
Brabanter Jos de
Goethals Ivan
Moor Bart de
Pelckmans Kristiaan
Suykens Johan A. K.
No associations
LandOfFree
Componentwise Least Squares Support Vector Machines does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Componentwise Least Squares Support Vector Machines, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Componentwise Least Squares Support Vector Machines will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-39771