学习了，谢谢

提供几遍论文的摘要，以供参考：
1、 S.S.Keerthi ..: This paper points out an importance source of confusion and inefficiency in Platt's Sequential Minimal Optimization (SMO) algorithm than is caused by the use of a single treshold value . Using clues from thd KKT conditions for thd dual problem ,two threshold parameters are employed to derive modifications SMO . These modified algorithms perform significatly faster than the original SMO on all benchmark datasets tried ..

2.   Decomposition methods are currently one of the major methods for training support vector machines.
They vary mainly according to different working set selections. Existing implementations
and analysis usually consider some specific selection rules. This article studies Sequential Minimal
Optimization (SMO)-type decomposition methods under a general and flexible way of choosing the
two-element working set. Main results include: 1) a simple asymptotic convergence proof, 2) a general
explanation of the shrinking and caching techniques, and 3) the linear convergence of the methods.
Extensions to some SVM variants are also discussed.

上遍的作者为：Pai-Hsuen Chen 等.
  3。  Rong-En Fan 等： Working set selection is an important step in decomposition methods for training support
vector machines (SVMs). This paper develops a new technique for working set selection in
SMO-type decomposition methods. It uses second order information to achieve fast con-
vergence. Theoretical properties such as linear convergence are established. Experiments
demonstrate that the proposed method is faster than existing selection methods using first
order information.

Leon Bottou
Chih-Jen Lin
Considerable eorts have been devoted to the implementation of an ecient optimization
method for solving the support vector machine dual problem. This chapter
proposes an in-depth review of the algorithmic and computational issues associated
with this problem. Besides this baseline, we also point out research directions that
are exploited more thoroughly in the rest of this book.