| 书目名称 | Machine Learning Foundations | | 副标题 | Supervised, Unsuperv | | 编辑 | Taeho Jo | | 视频video | http://file.papertrans.cn/621/620399/620399.mp4 | | 概述 | Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning.Outlines the computation paradigm for solving classification, regression, and clustering.Features esse | | 图书封面 |  | | 描述 | .This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning..Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;.Outlines the computation paradigm for solving classification, regression, and clustering;.Features essential techniques for building the a new generation of machine learning.. | | 出版日期 | Book 2021 | | 关键词 | Machine Learning; Supervised Learning; K nearest Neighbor; Naïve Bayes; Neural Networks; Support Vector M | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-65900-4 | | isbn_softcover | 978-3-030-65902-8 | | isbn_ebook | 978-3-030-65900-4 | | copyright | Springer Nature Switzerland AG 2021 |
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