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Titlebook: Machine Learning and Data Mining in Pattern Recognition; Second International Petra Perner Conference proceedings 2001 Springer-Verlag Berl

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發(fā)表于 2025-3-21 16:54:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Machine Learning and Data Mining in Pattern Recognition
副標(biāo)題Second International
編輯Petra Perner
視頻videohttp://file.papertrans.cn/621/620463/620463.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning and Data Mining in Pattern Recognition; Second International Petra Perner Conference proceedings 2001 Springer-Verlag Berl
描述This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001..The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and neural net based learning; learning for handwriting recognition; statistical and evolutionary learning; and content-based image retrieval.
出版日期Conference proceedings 2001
關(guān)鍵詞Algorithmic Learning; Classification; Clustering; Data Mining; Handwriting Recognition; Information Retri
版次1
doihttps://doi.org/10.1007/3-540-44596-X
isbn_softcover978-3-540-42359-1
isbn_ebook978-3-540-44596-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2001
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Machine Learning and Data Mining in Pattern Recognition978-3-540-44596-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
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https://doi.org/10.1007/3-540-44596-XAlgorithmic Learning; Classification; Clustering; Data Mining; Handwriting Recognition; Information Retri
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620463.jpg
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Concepts Learning with Fuzzy Clustering and Relevance Feedbackzzy rules, we incorporate the meta knowledge into a probabilistic relevance feedback approach to improve the retrieval performance. Results presented on synthetic and real databases show that our approach provides better retrieval precision compared to the case when no retrieval experience is used.
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0302-9743 Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001..The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and
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