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Titlebook: Algorithmic Learning Theory; 12th International C Naoki Abe,Roni Khardon,Thomas Zeugmann Conference proceedings 2001 Springer-Verlag Berlin

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樓主
發(fā)表于 2025-3-21 18:21:50 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Algorithmic Learning Theory
期刊簡稱12th International C
影響因子2023Naoki Abe,Roni Khardon,Thomas Zeugmann
視頻videohttp://file.papertrans.cn/153/152966/152966.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Algorithmic Learning Theory; 12th International C Naoki Abe,Roni Khardon,Thomas Zeugmann Conference proceedings 2001 Springer-Verlag Berlin
影響因子This volume contains the papers presented at the 12th Annual Conference on Algorithmic Learning Theory (ALT 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-located with the Fourth International Conference on Discovery Science (DS 2001). The volume includes 21 contributed papers. These papers were selected by the program committee from 42 submissions based on clarity, signi?cance, o- ginality, and relevance to theory and practice of machine learning. Additionally, the volume contains the invited talks of ALT 2001 presented by Dana Angluin of Yale University, USA, Paul R. Cohen of the University of Massachusetts at Amherst, USA, and the joint invited talk for ALT 2001 and DS 2001 presented by Setsuo Arikawa of Kyushu University, Japan. Furthermore, this volume includes abstracts of the invited talks for DS 2001 presented by Lindley Darden and Ben Shneiderman both of the University of Maryland at College Park, USA. The complete versions of t
Pindex Conference proceedings 2001
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Karl Fr. Hagenmüller,Gerhard Diepenhe concepts are not cyclical and hence can be expressed using a directed acyclic graph (not known to the learner). We investigate this learning problem in various popular theoretical models: mistake bound model, exact learningmo del and probably approximately correct (PAC) model.
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Learning Intermediate Conceptshe concepts are not cyclical and hence can be expressed using a directed acyclic graph (not known to the learner). We investigate this learning problem in various popular theoretical models: mistake bound model, exact learningmo del and probably approximately correct (PAC) model.
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0302-9743 2001), which was held in Washington DC, USA, during November 25–28, 2001. The main objective of the conference is to provide an inter-disciplinary forum for the discussion of theoretical foundations of machine learning, as well as their relevance to practical applications. The conference was co-loc
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Das Auslandsdienstleistungsgesch?ftorks under certain conditions. We give a much simpler analysis of the algorithm and simplify the conditions. From this simplification, we can provide a simpler algorithm, for which no prior knowledge on the quality of weak hypotheses is necessary.
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