Evaluation of error probability of classification based on the analysis of the bayes code

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Suppose that we have two training sequences generated by parametrized distributions P θ 1∗ and P θ 2∗, where θ 1∗{\ast} and θ 2∗{\ast} are unknown. Given training sequences, we study the problem of classifying whether a test sequence was generated according to P θ 1∗ or P θ 2∗. This problem can be thought of as a hypothesis testing problem and the weighted sum of type-I and type-II error probabilities is analyzed. To prove the results, we utilize the analysis of the codeword lengths of the Bayes code. It is shown that upper and lower bounds of the probability of error are characterized by the terms containing the Chernoff information, the dimension of a parameter space, and the ratio of the length between the training sequences and the test sequence. Further, we generalize the part of the preceding results to multiple hypotheses setup.

本文言語English
ホスト出版物のタイトル2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2510-2514
ページ数5
ISBN(電子版)9781728164328
DOI
出版ステータスPublished - 2020 6
イベント2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
継続期間: 2020 7 212020 7 26

出版物シリーズ

名前IEEE International Symposium on Information Theory - Proceedings
2020-June
ISSN(印刷版)2157-8095

Conference

Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
国/地域United States
CityLos Angeles
Period20/7/2120/7/26

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • 情報システム
  • モデリングとシミュレーション
  • 応用数学

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