Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees

研究成果: Article査読

14 被引用数 (Scopus)

抄録

In the distribution-independent model of concept learning of Valiant, Angluin and Laird have introduced a formal model of noise process, called classification noise process, to study how to compensate for randomly introduced errors, or noise, in classifying the example data. In this article, we investigate the problem of designing efficient learning algorithms in the presence of classification noise. First, we develop a technique of building efficient robust learning algorithms, called noise-tolerant Occam algorithms, and show that using them, one can construct a polynomial-time algorithm for learning a class of Boolean functions in the presence of classification noise. Next, as an instance of such problems of learning in the presence of classification noise, we focus on the learning problem of Boolean functions represented by decision trees. We present a noise-tolerant Occam algorithm for k-DL (the class of decision lists with conjunctive clauses of size at most k at each decision introduced by Rivest) and hence conclude that k-DL is polynomially learnable in the presence of classification noise. Further, we extend the noise-tolerant Occam algorithm for k-DL to one for r-DT (the class of decision trees of rank at most r introduced by Ehrenfeucht and Haussler) and conclude that r-DT is polynomially learnable in the presence of classification noise.

本文言語English
ページ(範囲)37-62
ページ数26
ジャーナルMachine Learning
11
1
DOI
出版ステータスPublished - 1993 4月
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

フィンガープリント

「Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル