Stream Data Mining: Algorithms And Their Probabilistic Properties
Language: English
Published by Springer International Publishing, 2019
- Hardcover
- New
Seller: moluna, Greven, Germanymoluna
AbeBooks seller since July 9, 2020
Condition: New
£ 135.96
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Add to basketItem description from seller
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 mathematically justifiedIs intended for a pr.
Seller Inventory # 267123712
- Title
- Stream Data Mining: Algorithms And Their Probabilistic Properties
- Author
- Leszek Rutkowski|Maciej Jaworski|Piotr Duda
- Publisher
- Springer International Publishing
- Publication year
- 2019
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 3030139611
- ISBN 13
- 9783030139612
- Series
- Book 50 of 95: Studies in Big Data
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Shipping rates from Germany to U.S.A.
| Item | 26 to 60 business days | 26 to 60 business days |
|---|---|---|
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