Constructing Kernel Functions for Binary Regression(Pattern Recognition)

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概要

• 論文の詳細を見る

• Kernel-based learning algorithms have been successfully applied in various problem domains, given appropriate kernel functions. In this paper, we discuss the problem of designing kernel functions for binary regression and show that using a bell-shaped cosine function as a kernel function is optimal in some sense. The rationale of this result is based on the Karhunen-Loeve expansion, i.e., the optimal approximation to a set of functions is given by the principal component of the correlation operator of the functions.

• 社団法人電子情報通信学会の論文
• 2006-07-01

著者

• SUGIYAMA Masashi

  Department of Computer Science, Tokyo Institute of Technology

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• Ogawa Hidemitsu

  Toray Engineering Co. Ltd.

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• OGAWA Hidemitsu

  Toray Engineering Co., Ltd.

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

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