Stochastic Complexity for tree models

Jun'Ichi Takeuchi, Andrew R. Barron

研究成果: 著書/レポートタイプへの貢献会議での発言

1 引用 (Scopus)

抄録

We study the problem of data compression, gambling and prediction of strings xn = x1x2...xn in terms of coding regret, where the tree model is assumed as a target class. We apply the minimax Bayes strategy for curved exponential families to this problem and show that it achieves the minimax regret without restriction on the data strings. This is an extension of the minimax result by (Takeuchi et al. 2013) for models of kth order Markov chains and determines the constant term of the Stochastic Complexity for the tree model.

元の言語英語
ホスト出版物のタイトル2014 IEEE Information Theory Workshop, ITW 2014
出版者Institute of Electrical and Electronics Engineers Inc.
ページ222-226
ページ数5
ISBN(電子版)9781479959990
DOI
出版物ステータス出版済み - 12 1 2014
イベント2014 IEEE Information Theory Workshop, ITW 2014 - Hobart, オーストラリア
継続期間: 11 2 201411 5 2014

出版物シリーズ

名前2014 IEEE Information Theory Workshop, ITW 2014

その他

その他2014 IEEE Information Theory Workshop, ITW 2014
オーストラリア
Hobart
期間11/2/1411/5/14

Fingerprint

Data compression
Markov processes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications

これを引用

Takeuchi, JI., & Barron, A. R. (2014). Stochastic Complexity for tree models. : 2014 IEEE Information Theory Workshop, ITW 2014 (pp. 222-226). [6970825] (2014 IEEE Information Theory Workshop, ITW 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITW.2014.6970825

Stochastic Complexity for tree models. / Takeuchi, Jun'Ichi; Barron, Andrew R.

2014 IEEE Information Theory Workshop, ITW 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 222-226 6970825 (2014 IEEE Information Theory Workshop, ITW 2014).

研究成果: 著書/レポートタイプへの貢献会議での発言

Takeuchi, JI & Barron, AR 2014, Stochastic Complexity for tree models. : 2014 IEEE Information Theory Workshop, ITW 2014., 6970825, 2014 IEEE Information Theory Workshop, ITW 2014, Institute of Electrical and Electronics Engineers Inc., pp. 222-226, 2014 IEEE Information Theory Workshop, ITW 2014, Hobart, オーストラリア, 11/2/14. https://doi.org/10.1109/ITW.2014.6970825
Takeuchi JI, Barron AR. Stochastic Complexity for tree models. : 2014 IEEE Information Theory Workshop, ITW 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 222-226. 6970825. (2014 IEEE Information Theory Workshop, ITW 2014). https://doi.org/10.1109/ITW.2014.6970825
Takeuchi, Jun'Ichi ; Barron, Andrew R. / Stochastic Complexity for tree models. 2014 IEEE Information Theory Workshop, ITW 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 222-226 (2014 IEEE Information Theory Workshop, ITW 2014).
@inproceedings{5fdd353deada48dc8d0d56ba6fac7879,
title = "Stochastic Complexity for tree models",
abstract = "We study the problem of data compression, gambling and prediction of strings xn = x1x2...xn in terms of coding regret, where the tree model is assumed as a target class. We apply the minimax Bayes strategy for curved exponential families to this problem and show that it achieves the minimax regret without restriction on the data strings. This is an extension of the minimax result by (Takeuchi et al. 2013) for models of kth order Markov chains and determines the constant term of the Stochastic Complexity for the tree model.",
author = "Jun'Ichi Takeuchi and Barron, {Andrew R.}",
year = "2014",
month = "12",
day = "1",
doi = "10.1109/ITW.2014.6970825",
language = "English",
series = "2014 IEEE Information Theory Workshop, ITW 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "222--226",
booktitle = "2014 IEEE Information Theory Workshop, ITW 2014",
address = "United States",

}

TY - GEN

T1 - Stochastic Complexity for tree models

AU - Takeuchi, Jun'Ichi

AU - Barron, Andrew R.

PY - 2014/12/1

Y1 - 2014/12/1

N2 - We study the problem of data compression, gambling and prediction of strings xn = x1x2...xn in terms of coding regret, where the tree model is assumed as a target class. We apply the minimax Bayes strategy for curved exponential families to this problem and show that it achieves the minimax regret without restriction on the data strings. This is an extension of the minimax result by (Takeuchi et al. 2013) for models of kth order Markov chains and determines the constant term of the Stochastic Complexity for the tree model.

AB - We study the problem of data compression, gambling and prediction of strings xn = x1x2...xn in terms of coding regret, where the tree model is assumed as a target class. We apply the minimax Bayes strategy for curved exponential families to this problem and show that it achieves the minimax regret without restriction on the data strings. This is an extension of the minimax result by (Takeuchi et al. 2013) for models of kth order Markov chains and determines the constant term of the Stochastic Complexity for the tree model.

UR - http://www.scopus.com/inward/record.url?scp=84929316288&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84929316288&partnerID=8YFLogxK

U2 - 10.1109/ITW.2014.6970825

DO - 10.1109/ITW.2014.6970825

M3 - Conference contribution

AN - SCOPUS:84929316288

T3 - 2014 IEEE Information Theory Workshop, ITW 2014

SP - 222

EP - 226

BT - 2014 IEEE Information Theory Workshop, ITW 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -