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Titlebook: Similarity Search and Applications; 10th International C Christian Beecks,Felix Borutta,Thomas Seidl Conference proceedings 2017 Springer I

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書目名稱Similarity Search and Applications
副標(biāo)題10th International C
編輯Christian Beecks,Felix Borutta,Thomas Seidl
視頻videohttp://file.papertrans.cn/868/867401/867401.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Similarity Search and Applications; 10th International C Christian Beecks,Felix Borutta,Thomas Seidl Conference proceedings 2017 Springer I
描述This book constitutes the refereed proceedings of the 10th International?Conference on Similarity Search and Applications, SISAP 2017, held in?Munich, Germany, in October 2017..The 23 full papers presented were?carefully reviewed and selected from 53 submissions. The papers deal with?issues surrounding the theory, design, analysis, practice, and application of content-based and feature-based similarity search.?They are organized in the following topical sections: approximate similarity search; improving similarity search methods and applications; distances for complex objects; outlier detection; indexing and applications; and applications and specific domains..The paper ‘A New Perspective on the Tree Edit Distance‘ is published open access under a CC BY 4.0 license at link.springer.com..
出版日期Conference proceedings 2017
關(guān)鍵詞applications; approximate similarity search; artificial intelligence; classification; data mining; databa
版次1
doihttps://doi.org/10.1007/978-3-319-68474-1
isbn_softcover978-3-319-68473-4
isbn_ebook978-3-319-68474-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
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r, motivation and achievements in chemistry” by Devetak and Gla?ar who described other aspects of students’ characteristics using statistical analysis of numerous variables regarding triple nature of chemical concepts presentations. Students can be differentiated into groups during classroom activit
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Michael E. Houle,Vincent Oria,Arwa M. Waliassical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation abi
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assical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation abi
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