I have started reading Vladimir Vapnik’s Statistical Learning Theory book. It is a fascinating book which I enjoy much whenever I read it. Parallel to it, I read Vidyasagar’s Learning and Generalization (2nd edition). It is also about statistical learning theory. However, its trend is somehow different from Vapnik’s. It is early to judge and compare these books, but from what I have read up to now, I can say that Vapnik’s book is much easier and insightful comparing the other. On the other hand, Vidyasagar’s book is more mathematically inclined and I cannot understand many parts of it easily (so I escape most proofs and …). A big problem (for me) in Vidyasagar’s books is that it does not try to explain the underlying phenomena intuitively.
Would you mind write your idea and suggestions about these two books? Moreover, I want to become aware of trends in the theoretical ML. Which book do you suggest? (Kearns?! I haven’t it and I don’t know from where I can find it.)
From the list of statistical learning theory books here (http://www.campusi.com/keyword_Statistical_Learning_Theory.htm)
I can recommend Christianini’s book and Duda/Hart’s book. Christianini’s book just has one chapter on generalization theory, but it has a lot of practical information, such as optimization theory.
Duda Hart’s book isn’t really on statistical learning theory, but more of a Bayesian approach, and is considered a classic.
TnX! (:
I try to do so!
Good luck!