| 书目名称 | Empirical Approach to Machine Learning | | 编辑 | Plamen P. Angelov,Xiaowei Gu | | 视频video | | | 概述 | New efficient methods for pattern recognition and machine learning in data-rich environments.Focuses on automated methods, which can be easily adapted to various applications.Covers techniques with hi | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. Itcan also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code..Dimit | | 出版日期 | Book 2019 | | 关键词 | Empirical Data Analytics; Data-centered Approaches; Deep Learning Applications; Fuzzy Rule-based Classi | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-02384-3 | | isbn_softcover | 978-3-030-13209-5 | | isbn_ebook | 978-3-030-02384-3Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | Springer Nature Switzerland AG 2019 |
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发表于 2025-3-21 18:12:25
