Description: Stream Data Mining: Algorithms and Their Probabilistic Properties Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Leszek Rutkowski, Maciej Jaworski, Piotr Duda Format: Hardback Publisher: Springer Nature Switzerland AG, Switzerland Imprint: Springer Nature Switzerland AG ISBN-13: 9783030139612, 978-3030139612 Synopsis 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, [url] in telecommunication, banking, and sensor networks.
Price: 119.69 GBP
Location: Aldershot
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Book Title: Stream Data Mining: Algorithms and Their Probabilistic Properties
Item Height: 235 mm
Item Width: 155 mm
Series: Studies in Big Data
Author: Maciej Jaworski, Leszek Rutkowski, Piotr Duda
Publication Name: Stream Data Mining: Algorithms and Their Probabilistic Properties
Format: Hardcover
Language: English
Publisher: Springer Nature Switzerland A&G
Subject: Engineering & Technology, Computer Science, Business
Publication Year: 2019
Type: Textbook
Item Weight: 676 g
Number of Pages: 330 Pages
