Learning with membership queries to minimize prediction error

Yoshifumi Ukita, Toshiyasu Matsushima, Shigeichi Hirasawa

Research output: Contribution to journalConference article

Abstract

In this paper, we consider the problem to predict the class of an unknown sample after learning from queries. We propose to evaluate a learning algorithm by a loss function for the prediction under a constraint. In this paper, the error probability for the prediction and the number of queries is defined as the loss function and the constraint, respectively. Then our objective is to minimize the error probability, the error probability is determined by what presentation order for instances to query and how to predict. Since the optimal prediction has been shown in previous researches, we only have to select the optimal presentation order for instances to query. We propose a lower bound used in the branch-and-bound algorithm to select the optimal presentation order for instances. Lastly, we show the efficiency of the algorithm using the derived lower bound by numerical computation.

Original languageEnglish
Pages (from-to)4412-4417
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume5
Publication statusPublished - 1997 Dec 1
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) - Orlando, FL, USA
Duration: 1997 Oct 121997 Oct 15

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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