Pattern Recognition and Machine Learning(2006).pdf
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
- uniken082013-08-04入门级学习资料,收藏了
- shashadedandan2013-02-03很有用,对于学习Pattern Recognition 很有帮助
- ipuma2013-08-06Pattern Recognition和Machine learning方面的经典教材,非常感谢分享!
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