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Titlebook: Learning Theory; 17th Annual Conferen John Shawe-Taylor,Yoram Singer Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 2004 Boo

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發(fā)表于 2025-3-21 18:33:05 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Learning Theory
副標題17th Annual Conferen
編輯John Shawe-Taylor,Yoram Singer
視頻videohttp://file.papertrans.cn/583/582820/582820.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Learning Theory; 17th Annual Conferen John Shawe-Taylor,Yoram Singer Conference proceedings 2004 Springer-Verlag Berlin Heidelberg 2004 Boo
出版日期Conference proceedings 2004
關鍵詞Boolean function; Boosting; algorithmic learning; bayesian networks; computational learning; decision the
版次1
doihttps://doi.org/10.1007/b98522
isbn_softcover978-3-540-22282-8
isbn_ebook978-3-540-27819-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2004
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:36:59 | 只看該作者
A Function Representation for Learning in Banach Spacesh spaces and show how this function representation naturally arises in that problem. Furthermore, we provide circumstances in which these representations are dense relative to the uniform norm and discuss how the parameters in such representations may be used to fit data.
板凳
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地板
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Regret Bounds for Hierarchical Classification with Linear-Threshold Functions the number of training examples and depends in a detailed way on the interaction between the process parameters and the taxonomy structure. Preliminary experiments on real-world data provide support to our theoretical results.
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Concentration Bounds for Unigrams Language Modelarning algorithm is its expected per-word log-loss. We show that the leave-one-out method can be used for estimating the log-loss of the unigrams model with a PAC error of approximately ., for any distribution..We also bound the log-loss a priori, as a function of various parameters of the distribution.
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Learning Classes of Probabilistic Automata stochastic languages. We show that a MA may generate a stochastic language that cannot be generated by a PFA, but we show also that it is undecidable whether a MA generates a stochastic language. Finally, we propose a learning algorithm for a subclass of PFA, called PRFA.
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