Comments on "Pruning Error Minimization in Least Squares Support Vector Machines

Author

Kuh, Anthony ; De Wilde, Philippe

Author_Institution

Univ. of Hawaii, Honolulu

Volume

18

Issue

2

fYear

2007

fDate

3/1/2007 12:00:00 AM

Firstpage

606

Lastpage

609

Abstract

In this letter, we comment on "Pruning Error Minimization in Least Squares Support Vector Machines" by B. J. de Kruif and T. J. A. de Vries. The original paper proposes a way of pruning training examples for least squares support vector machines (LS SVM) using no regularization (gamma = infin). This causes a problem as the derivation involves inverting a matrix that is often singular. We discuss a modification of this algorithm that prunes with regularization (gamma finite and nonzero) and is also computationally more efficient.

Keywords

least squares approximations; support vector machines; error minimization; least squares support vector machines; training examples; Computational complexity; Cost function; Councils; Equations; Kernel; Least squares methods; Optimization methods; Quadratic programming; Sparse matrices; Support vector machines; Least squares kernel methods; online updating; pruning; regularization; Algorithms; Computer Simulation; Feedback; Information Storage and Retrieval; Least-Squares Analysis; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.891590

Filename

4118265