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.
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。
評分内容不多,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。
評分内容不多,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。
評分内容不多,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。
評分内容不多,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。
因為科研要用看瞭一半 這輩子都不會忘記GPR瞭...
评分隻讀瞭regression那章
评分對我來說還是挺難的,被評論區打擊得不行= =
评分比起PRML實用性很強,看起來思路也很清晰有條理。
评分Nice!各方麵都非常棒,期待第二版!加入一些DGP還有gaussian +dL的東西可能更有趣
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