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Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Carlos Soa Conference p

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书目名称Machine Learning and Knowledge Discovery in Databases
副标题European Conference,
编辑Annalisa Appice,Pedro Pereira Rodrigues,Carlos Soa
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Carlos Soa Conference p
描述The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
出版日期Conference proceedings 2015
关键词data mining; foundations of machine learning and data mining; knowledge discovery in databases; probabi
版次1
doihttps://doi.org/10.1007/978-3-319-23525-7
isbn_softcover978-3-319-23524-0
isbn_ebook978-3-319-23525-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Machine Learning and Knowledge Discovery in Databases978-3-319-23525-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond cus is on the computational aspects of the theory and studying the computational complexity and approximability of many problems related to generalized matrix factorizations. The results immediately apply to a large number of data mining problems, and hopefully allow generalizing future results and algorithms, as well.
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Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithmsifs underlying the kernel predictor. We demonstrate the efficacy of our approach through a series of experiments on synthetic and real data, including problems from handwritten digit recognition and a large-scale . splice site data set from the domain of computational biology.
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