Stream Data Mining Algorithms by Rutkowski Leszek (18 results)
Language: English
Published by Springer, 2019
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Published by Springer, 2019
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Published by Springer, 2019
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Published by Springer, 2019
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Language: English
Published by Springer, 2019
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Published by Springer International Publishing, Springer Nature Switzerland, 2019
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt st…atic decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks.
Stream Data Mining: Algorithms and Their Probabilistic Properties
Rutkowski, Leszek (Author)/ Jaworski, Maciej (Author)/ Duda, Piotr (Author)
Language: English
Published by Springer, 2019
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Published by Springer, 2019
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Language: English
Published by Springer, 2019
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Language: English
Published by Springer, 2019
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Published by Springer, 2019
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Published by Springer International Publishing, 2019
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a unique and innovative approach to stream data miningUnlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematic…ally justifiedIs intended for a pr.
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Language: English
Published by Springer International Publishing Mrz 2019, 2019
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes… how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks. 340 pp. Englisch.
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Language: English
Published by Springer International Publishing, Springer Nature Switzerland Mär 2019, 2019
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how… to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.
Language: English
Published by Springer, 2019
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Language: English
Published by Springer, 2019
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