| 书目名称 | Fundamentals of Pattern Recognition and Machine Learning | | 编辑 | Ulisses Braga-Neto | | 视频video | | | 概述 | Strikes a balance between theory and practice, using Python scripts, bioinformatics and data sets to illustrate key points.Includes supplementary material: .Request lecturer material: | | 图书封面 |  | | 描述 | .Fundamentals of Pattern Recognition and Machine Learning. is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at the graduate or advanced undergraduate level. The book combines theory and practice and is suitable to the classroom and self-study. It has grown out of lecture notes and assignments that the author has developed while teaching classes on this topic for the past 13 years at Texas A&M University. .The book is intended to be concise but thorough. It does not attempt an encyclopedic approach, but covers in significant detail the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as Gaussian process regression and convolutional neural networks. In addition, the selection of topics has a few features that are unique among comparable texts: it contains an extensive chapter on classifiererror estimation, as well as sections on Bayesian classification, Bayesian error estimation, separate sampling, and rank-based classification..The book is mathematically rigorous and covers the classical theorems in | | 出版日期 | Textbook 20201st edition | | 关键词 | Pattern Recognition; Machine Learning; Classification; Regression; Clustering; Feature Selection; Error Es | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-27656-0 | | isbn_softcover | 978-3-030-27658-4 | | isbn_ebook | 978-3-030-27656-0 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 17:06:52
