Getting your feet wet with SVM

This is a direct copy-paste from the Kernel Machines site:

There are several introductions, review papers, and books. As usual, there is a trade-off between how much time you want to invest, and how much you will get out of it. The following list is ordered by increasing time. On the log(time) domain, the increase in effort should be pretty much linear. ;)
(a) The introduction of the book Advances in Kernel Methods - Support Vector Learning or the high level overview of Hearst et al. from IEEE Intelligent Systems
 
(b) The tutorial papers of Burges (SVM pattern recognition) or Smola and Schoelkopf (SVM regression estimation)
(c) The small book of Vapnik, published by Springer (1995, or, in second edition, 1999), or the one of Cristianini and Shawe-Taylor (2000).
Alternatives that can be downloaded free of charge are PhD theses on SVMs, such as the ones of Schoelkopf (1997), Smola (1998), Herbrich (2000, to be finished soon).
(d) The collection of papers presented at the NIPS workshop on SVMs and kernel methods, from 1997 (Advances in Kernel Methods, MIT Press, 1999), 1998 (Advances in Large Margin Classifiers, MIT Press, 2000 - to appear), or 1999 (these will appear in a special issue of the journal Machine Learning at some point of time in the future).
(e) The long book of Vapnik, the bible of statistical learning theory (Wiley, 1998).