Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) pdf epub mobi txt 电子书 下载 2025
- 机器学习
- GaussianProcess
- 高斯过程
- MachineLearning
- 统计学习
- Gaussian
- ML
- 人工智能
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.
具体描述
读后感
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。
评分内容不多，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。
评分内容不多，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。
评分内容不多，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。
评分内容不多，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。
用户评价
比起PRML实用性很强，看起来思路也很清晰有条理。
评分最好的GP教材
评分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
评分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
评分个别步骤跳的有点狠,概率论基础差的建议先好好复习以下多元的高斯分布
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