| 书目名称 | Stream Data Mining: Algorithms and Their Probabilistic Properties | | 编辑 | Leszek Rutkowski,Maciej Jaworski,Piotr Duda | | 视频video | | | 概述 | Presents a unique and innovative 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 ma | | 丛书名称 | Studies in Big Data | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2020 | | 关键词 | Big Data; Data Science; Stream Data Mining; Streaming; Stream Data Algorithms | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-13962-9 | | isbn_ebook | 978-3-030-13962-9Series ISSN 2197-6503 Series E-ISSN 2197-6511 | | issn_series | 2197-6503 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 17:22:18
