Stream Data Mining: Algorithms and Their Probabilistic Properties: 56 (Studies in Big Data, 56) - Tapa dura

Libro 50 de 95: Studies in Big Data

Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr

 
9783030139612: Stream Data Mining: Algorithms and Their Probabilistic Properties: 56 (Studies in Big Data, 56)
Tapa dura
ISBN 10: 3030139611 ISBN 13: 9783030139612
Editorial: Springer, 2019

Sinopsis

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 deal with stream data, e.g. in telecommunication, banking, and sensor networks.

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De la contraportada

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.


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Editorial
Springer
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030139611
ISBN 13 
9783030139612
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
342
Contacto del fabricante
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