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

Anthony Kuh, Philippe De Wilde

Research output: Contribution to journalComment/debatepeer-review

56 Citations (Scopus)


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 languageEnglish
Pages (from-to)606-609
Number of pages4
JournalIEEE Transactions on Neural Networks
Issue number2
Publication statusPublished - Mar 2007


  • Least squares kernel methods
  • Online updating
  • Pruning
  • Regularization


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