Kernel ridge regression using truncated newton method

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24 Scopus citations

Abstract

Kernel Ridge Regression (KRR) is a powerful nonlinear regression method. The combination of KRR and the truncated-regularized Newton method, which is based on the conjugate gradient (CG) method, leads to a powerful regression method. The proposed method (algorithm), is called Truncated-Regularized Kernel Ridge Regression (TR-KRR). Compared to the closed-form solution of KRR, Support Vector Machines (SVM) and Least-Squares Support Vector Machines (LS-SVM) algorithms on six data sets, the proposed TR-KRR algorithm is as accurate as, and much faster than all of the other algorithms.

Original languageBritish English
Pages (from-to)339-344
Number of pages6
JournalKnowledge-Based Systems
Volume71
DOIs
StatePublished - 1 Nov 2014

Keywords

  • Kernel methods
  • Kernel ridge regression
  • Least-squares
  • Regression
  • Truncated Newton

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