Ellipsoidal support vector machines

Michinari Momma, Kohei Hatano, Hiroki Nakayama

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

This paper proposes the ellipsoidal SVM (e-SVM) that uses an ellipsoid center, in the version space, to approximate the Bayes point. Since SVM approximates it by a sphere center, e-SVM provides an extension to SVM for better approximation of the Bayes point. Although the idea has been mentioned before (Ruján (1997)), no work has been done for formulating and kernelizing the method. Starting from the maximum volume ellipsoid problem, we successfully formulate and kernelize it by employing relaxations. The resulting e-SVM optimization framework has much similarity to SVM; it is naturally extendable to other loss functions and other problems. A variant of the sequential minimal optimization is provided for efficient batch implementation. Moreover, we provide an online version of linear, or primal, e-SVM to be applicable for large-scale datasets.

Original languageEnglish
Pages (from-to)31-46
Number of pages16
JournalJournal of Machine Learning Research
Volume13
Publication statusPublished - Dec 1 2010
Event2nd Asian Conference on Machine Learning, ACML 2010 - Tokyo, Japan
Duration: Nov 8 2010Nov 10 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Ellipsoidal support vector machines'. Together they form a unique fingerprint.

  • Cite this