A METHOD FOR SUPPORT VECTOR MACHINE CLASSIFICATION WITH INDEFINITE KERNELS

Kimura Saeko; Hiroshi Yabe

doi:10.15807/torsj.55.110

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A METHOD FOR SUPPORT VECTOR MACHINE CLASSIFICATION WITH INDEFINITE KERNELS

Kimura Saeko Hiroshi Yabe

Year: 2012 Journal: Transactions of the Operations Research Society of Japan Vol: 55 (0)Pages: 110-131

DOI: 10.15807/torsj.55.110

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Abstract

サポートベクターマシン(SVM)は2値分類問題を解くための手法である.SVMにおいて最も重要であるのは,適したカーネル行列の選び方であり,一般に半正定値のものが用いられる.しかし不定値のカーネル行列がデータの特性を表していることも多く,その場合のSVMのための様々な手法が考えられている.本論文では,その手法の一つであるLuss and d'Aspremondtのモデルを紹介し,その解法として射影勾配BB法を提案する.また,Lussらのモデルは計算量の点で問題があることから,その修正として新たなモデルを定式化し,数値実験により識別能力を評価する.

Keywords:

Support vector machine Artificial intelligence Computer science Structured support vector machine Pattern recognition (psychology) Ranking SVM Machine learning Data mining

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Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

A METHOD FOR SUPPORT VECTOR MACHINE CLASSIFICATION WITH INDEFINITE KERNELS

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