Those material work for svm beginner, material concerned with newly and learning theory excluded. if you are willing to study in a deep way, you should get more material from google schoolar etc.
thesis:
Support Vector Networks, Vapnik etc. (original paper)
A Tutorial on Support Vector Machines for Pattern Recognition, C. J. Burges
Large-Scale Support Vector Machines: Algorithms and Theory, Aditya Krishna Menon (very good survey, mainly on parameter inference)
Training a Support Vector Machine in the Primal
BudgetedSVM: A Toolbox for Scalable SVM Approximations, Nemanja Djuric
Making Large-Scale SVM Learning Practical, T. Joachims
Sequential Minimal Optimization for SVM, (author missing), (detailed parameter inference based on SMO)
Fast Training of Support Vector Machines using Sequential Minimal Optimization, John C. Platt, (SMO orginal paper)
Working Set Selection Using Second Order Information for Training Support Vector Machines, (workset seletion method for libsvm, I am not sure still using it)
LIBSVM: A Library for Support Vector Machines, Chih-Chung Chang etc, libsvm paper
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM, Shai Shalev-Shwartz etc., (Pegasos, gradient based techique)
SGD-QN, LaRank, Antoine Bordes and Léon Bottou, (gradient based method, using quasi-Newton, Hessian matrix and LBFGS involved
site:
Support Vector Machine, Wikipedia terms, http://en.wikipedia.org/wiki/Support_vector_machine
SVM org, http://www.support-vector-machines.org/
Leon Bottou homepage, http://leon.bottou.org/ (very good site)
book:Pattern Recognition and Machine learning, C. Bishop
Kernel Methods for Pattern Analysis, J. Shawe-Taylor and N. Cristianini
Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms, T. Joachims
tools:you can get list of tools for svm: http://www.support-vector-machines.org/SVM_soft.html
libsvm
weka
svm light
Machout
原文：http://www.cnblogs.com/wjgaas/p/4310163.html