统计学习理论的本质
The Nature of Statistical Learning Theory
Vladimir Naumovich Vapnik · 初版 1995 年
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本书旨在探讨统计学习理论与泛化理论背后的基本思想。它从基于经验数据进行函数估计的全局视角来审视学习。作者略去了证明和技术细节，重点讨论学习理论的主要成果及其与统计学基本问题的联系。其中包括：学习问题的一般设定以及基于经验数据最小化风险泛函的通用模型；经验风险最小化方法的全面分析
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如果你刚好数学比编程强得多，那从理论切入可能会容易得多。这篇综述是我见过的最好的入门资料。另一个选择是读瓦普尼克的《统计学习理论的本质》（The Nature of Statistical Learning Theory），那本书篇幅更长，而且风格要“俄式”得多。吴恩达的讲义则是通用机器学习方面极好的（非理论）入门材料。http://www.stanford.edu/class/cs229/materials.html
If you happen to be much better at math than programming, starting with the theory could be much easier. This survey is the best introduction I have found. The alternative being to read Vapnik's "The Nature of Statistical Learning Theory", which is more longer and much more "Russian". Andrew Ng's notes are an excellent (non-theory) introduction to general machine learning. http://www.stanford.edu/class/cs229/materials.html
……理论（网上有免费手稿），里面的内容大同小异，但在证明之外包裹了相当不错的动机阐释和框架解释。我也很喜欢瓦普尼克的《统计学习理论的本质》（The Nature of Statistical Learning Theory），据我理解，它把大部分相同的内容以更长、偏重证明的形式平行呈现了出来。相比我本科统计课的讲法，我更喜欢这本书。再次，……
…theory (available as a free manuscript online), which had a lot of the same kind of material with quite a good framework of motivation and explanation wrapped around its proofs. I also like Vapnik's _The Nature of Statistical Learning Theory_ which as I understand it is highly parallel to a much longer proof-heavy version of most of the same material. I much preferred this book to the approach in my undergraduate course in statistics. Again,…
……限制太严格了！我觉得MDL应该会很有用，但务必搞清楚我在书里描述的那种“手动过拟合（manual overfitting）”的危险。我也建议你去读读瓦普尼克的《统计学习理论的本质》（The Nature of Statistical Learning Theory），里面展示了MDL与VC理论之间的关系。欢迎随时发邮件到 firstname dot lastname at gmail.com 找我，我随时乐意聊聊这些想法。
…limits are so strict! I think MDL should be very helpful, but make sure you understand the danger of "manual overfitting" that I described in the book. I would also encourage you to read Vapnik's book the Nature of Statistical Learning Theory, which shows the relation b/t MDL and VC theory. Feel free to reach out to me at firstname dot lastname at gmail.com, I'm always happy to chat about these ideas.
……统计推断，其实并非如此。我想证明，在这一科学领域中，一条古老而优秀的原则依然适用：没有什么比一个好理论更实用了。——摘自瓦普尼克为《统计学习理论的本质》写的序言 * 把瓦普尼克简单描述成一个“搞理论的人”并不贴切。那听起来好像他对理论与实践的结合不感兴趣，但事实绝非如此。他可以说是……
…statistical inference, this is not true. I would like to demonstrate that in this area of science a good old principle is valid: Nothing is more practical than a good theory. -- From Vapnik's preface to The Nature of Statistical Learning Theory * Vapnik is not well-described as a "theory guy". That implies that he's not interested in connections between theory and practice, and this is most profoundly not the case. He has arguably been the…
……参考文献。随手Google了一下，在 http://vserver1.cscs.lsa.umich.edu/~crshalizi/reviews/vapnik... 找到了一篇书评：The Bactra Review，科斯马·沙利齐（Cosma Shalizi）撰写的不定期杂食书评，评V. N. 瓦普尼克的《统计学习理论的本质》（The Nature of Statistical Learning Theory）。好吧，瓦普尼克试图用一些数学推导来证明统计学习理论（可能特别是其中某些算法）的一些理想数学性质。挺好的。……
…the reference. From a simple Google, found at http://vserver1.cscs.lsa.umich.edu/~crshalizi/reviews/vapnik... a review The Bactra Review: Occasional and eclectic book reviews by Cosma Shalizi The Nature of Statistical Learning Theory by V. N. Vapnik Okay, Vapnik is trying to do some mathematics to show some desirable mathematical properties of some of statistical learning theory, maybe in particular some of the algorithms. Good.…
……《统计学习基础：数据挖掘、推断与预测》（Elements of Statistical Learning Data: Mining, Inference, and Prediction）第二版，Springer，2008年。我发现提到了瓦普尼克，主要是关于分离超平面的，并引用了 Vapnik, V. (1996). 《统计学习理论的本质》（The Nature of Statistical Learning Theory）, Springer, New York. 以及 Vapnik, V. (1998). 《统计学习理论》（Statistical Learning Theory）, Wiley, New York. 看看 Hastie 等人的书，感觉相对偏基础，我本来也没指望能在里面找到太多关于库恩-塔克（Kuhn-Tucker）……
…Elements of Statistical Learning Data: Mining, Inference, and Prediction, Second Edition, Springer, 2008. I find mention of Vapnik, mostly for separating hyperplanes, and references Vapnik, V. (1996). The Nature of Statistical Learning Theory, Springer, New York. Vapnik, V. (1998). Statistical Learning Theory, Wiley, New York. Looking at Hastie, et al., which seems relatively elementary, I wouldn't expect to find much on the Kuhn-Tucker…