Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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简体网页||繁体网页

Carl Edward Rasmussen 作者

The MIT Press

译者

2005-12-01 出版日期

244 页数

USD 36.00 价格

Hardcover

Adaptive Computation and Machine Learning 丛书系列

9780262182539 图书编码

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书图书标签: 机器学习 GaussianProcess 高斯过程 MachineLearning 统计学习 Gaussian ML 人工智能

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发表于2024-11-22

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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书用户评价

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

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对我来说还是挺难的，被评论区打击得不行= =

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Nice!各方面都非常棒，期待第二版！加入一些DGP还有gaussian +dL的东西可能更有趣

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have a go with it if you are really interested in predicting the unknown.=］Need any examples? well, your longevity,stock market,weather forecast.....countless really..=P

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最好的GP教材

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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书图书描述

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.

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书下载 mobi epub pdf txt 在线电子书下载

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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) 在线电子书读后感

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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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