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Titlebook: Intelligence Science II; Third IFIP TC 12 Int Zhongzhi Shi,Cyriel Pennartz,Tiejun Huang Conference proceedings 2018 IFIP International Fede

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书目名称Intelligence Science II
副标题Third IFIP TC 12 Int
编辑Zhongzhi Shi,Cyriel Pennartz,Tiejun Huang
视频video
丛书名称IFIP Advances in Information and Communication Technology
图书封面Titlebook: Intelligence Science II; Third IFIP TC 12 Int Zhongzhi Shi,Cyriel Pennartz,Tiejun Huang Conference proceedings 2018 IFIP International Fede
描述This book constitutes the refereed proceedings of the Third International Conference on Intelligence Science, ICIS 2018, held in Beijing China, in November 2018..The 44 full papers and 5 short papers presented were carefully reviewed and selected from 85 submissions. They deal with key issues in intelligence science and have been organized in the following topical sections: brain cognition; machine learning; data intelligence; language cognition; perceptual intelligence; intelligent robots; fault diagnosis; and ethics of artificial intelligence..
出版日期Conference proceedings 2018
关键词artificial intelligence; clustering; computer networks; data mining; data security; heuristic methods; Hum
版次1
doihttps://doi.org/10.1007/978-3-030-01313-4
isbn_softcover978-3-030-13167-8
isbn_ebook978-3-030-01313-4Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2018
The information of publication is updating

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978-3-030-13167-8IFIP International Federation for Information Processing 2018
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Xiaolin Xu,Yan Liu,Jiali Fengen und aus den Fallstudien wertvolle Erkenntnisse und Best Practices für eine eigene Weiterentwicklung gewinnen..Das Buch wendet sich zum einen an Praktiker aus Wirtschaftsprüfungsgesellschaften und multination978-3-658-23155-2978-3-658-23156-9Series ISSN 2946-0301 Series E-ISSN 2946-031X
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From Bayesian Inference to Logical Bayesian Inferencet to the Maximum Likelihood (ML) criterion, and compatible with the Regularized Least Square (RLS) criterion. By matching the two channels one with another, we can obtain the Channels’ Matching (CM) algorithm. This algorithm can improve multi-label classifications, maximum likelihood estimations (in
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Semantic Channel and Shannon’s Channel Mutually Match for Multi-label Classificationive, negative, and unclear) instead of two kinds as in the One-vs-Rest or Binary Relevance (BR) method. Every label’s learning is independent as in the BR method. However, it is allowed to train a label without negative examples and a number of binary classifications are not used. In the label selec
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