Mendeley出力
＜図書＞
The minimum description length principle
| 責任表示 | Peter D. Grünwald |
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| シリーズ | Adaptive computation and machine learning |
| データ種別 | 図書 |
| 出版情報 | Cambridge, Mass. : MIT Press , c2007 |
| 本文言語 | 英語 |
| 大きさ | xxxii, 703p. ; 24cm |
| 概要 | The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explana...ion, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can be arbitrarily complex, and overfitting the data is a serious concern.続きを見る |
所蔵情報
| 状態 | 巻次 | 所蔵場所 | 請求記号 | 刷年 | 文庫名称 | 資料番号 | コメント | 予約・取寄 | 複写申込 | 自動書庫 |
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理系図1F 開架 | 007.1/G 75 | 2007 |
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031212009000325 |
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書誌詳細
| 一般注記 | Foreword by Jorma Rissanen Includes bibliographical references and index |
|---|---|
| 著者標目 | Grünwald, Peter D. Rissanen, Jorma |
| 件 名 | LCSH:Minimum description length (Information theory) |
| 分 類 | LCC:QA276.9 DC22:003/.54 NDLC:M121 |
| 書誌ID | 1001318689 |
| ISBN | 9780262072816 |
| NCID | BA81626420 |
| 巻冊次 | ISBN:9780262072816 |
| 登録日 | 2009.09.18 |
| 更新日 | 2009.09.18 |