| 書目名稱 | Statistical and Inductive Inference by Minimum Message Length | | 編輯 | C.S. Wallace | | 視頻video | http://file.papertrans.cn/877/876679/876679.mp4 | | 概述 | Since 1965, Professor Wallace and others have been developing an approach tostatistical estimation, hypothesis testing, model selection and their applications in the Artificial Intelligence field of M | | 叢書名稱 | Information Science and Statistics | | 圖書封面 |  | | 描述 | Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks’ arguing our cases, we decided | | 出版日期 | Book 2005 | | 關鍵詞 | Computer; Information; Sage; data mining; formal specification; learning; machine learning | | 版次 | 1 | | doi | https://doi.org/10.1007/0-387-27656-4 | | isbn_softcover | 978-1-4419-2015-7 | | isbn_ebook | 978-0-387-27656-4Series ISSN 1613-9011 Series E-ISSN 2197-4128 | | issn_series | 1613-9011 | | copyright | Springer-Verlag New York 2005 |
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