Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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Carl Edward Rasmussen 作者

The MIT Press

譯者

2005-12-01 出版日期

244 頁數

USD 36.00 價格

Hardcover

Adaptive Computation and Machine Learning 叢書系列

9780262182539 圖書編碼

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書圖書標籤: 機器學習 GaussianProcess 高斯過程 MachineLearning 統計學習 Gaussian ML 人工智能

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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

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評分☆☆☆☆☆

最好的GP教材

評分☆☆☆☆☆

易讀（從machine learning）角度。深度不夠（從數學角度）

評分☆☆☆☆☆

易讀（從machine learning）角度。深度不夠（從數學角度）

評分☆☆☆☆☆

最好的GP教材

評分☆☆☆☆☆

"Engineers doing statistics by another name." -- BDR And I can't agree more...:P

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書著者簡介

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在線電子書圖書描述

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

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内容不多，毕竟只有薄薄一本，有一定的实际参考价值，是一本还可以的入门书籍。如果本身对于Kernel的方法以及统计的学习方法有一定的理解的话，看这个会觉得有些简单了。和Bishop的书相比，内容和语言上，个人觉得还有一定的差距。

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