| 书目名称 | Machine Learning Methods | | 编辑 | Hang Li | | 视频video | | | 概述 | Provides introduction to principle machine learning methods, covering both supervised and unsupervised learning methods.Presents clear descriptions, detailed proofs, and concrete examples using concis | | 图书封面 |  | | 描述 | This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis.. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic princip | | 出版日期 | Textbook 2024 | | 关键词 | Machine Learning; Statistical Learning; Supervised Learning; Unsupervised Learning; Classification; Regre | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-99-3917-6 | | isbn_softcover | 978-981-99-3919-0 | | isbn_ebook | 978-981-99-3917-6 | | copyright | Tsinghua University Press 2024 |
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发表于 2025-3-21 16:19:35
