Comments on "Pruning error minimization in least squares support vector machines"

Anthony Kuh; Philippe De Wilde

doi:10.1109/TNN.2007.891590

Comments on "Pruning error minimization in least squares support vector machines"

Anthony Kuh, Philippe De Wilde

Research output: Contribution to journal › Comment/debate › peer-review

57 Citations (Scopus)

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 (? = 8). 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 (? finite and nonzero) and is also computationally more efficient. © 2007 IEEE.

Original language	English
Pages (from-to)	606-609
Number of pages	4
Journal	IEEE Transactions on Neural Networks
Volume	18
Issue number	2
DOIs	https://doi.org/10.1109/TNN.2007.891590
Publication status	Published - Mar 2007

Keywords

Least squares kernel methods
Online updating
Pruning
Regularization

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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 (? = 8). 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 (? finite and nonzero) and is also computationally more efficient. {\textcopyright} 2007 IEEE.",

keywords = "Least squares kernel methods, Online updating, Pruning, Regularization",

author = "Anthony Kuh and {De Wilde}, Philippe",

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AB - 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 (? = 8). 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 (? finite and nonzero) and is also computationally more efficient. © 2007 IEEE.

KW - Least squares kernel methods

KW - Online updating

KW - Pruning

KW - Regularization

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EP - 609

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

IS - 2

ER -

Comments on "Pruning error minimization in least squares support vector machines"

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