### abstract ###
In the process of training Support Vector Machines~(SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field
To improve working set selection, we propose a new model for working set selection in sequential minimal optimization~(SMO) decomposition methods
In this model, it selects  SYMBOL  as % SYMBOL  as working set without reselection
Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods
### introduction ###
In the past few years, there has been huge of interest in Support Vector Machines~(SVMs)~ CITATION  because they have excellent generalization performance on a wide range of problems
The key work in training SVMs is to solve the follow quadratic optimization problem
where  SYMBOL  is the vector of all ones,  SYMBOL  is the upper bound of all variables, and  SYMBOL ,  SYMBOL  is the kernel function
Notable effects have been taken into training SVMs~ CITATION
Unlike most optimization methods which update the whole vector  SYMBOL  in each iteration, the decomposition method modifies only a subset of  SYMBOL  per iteration
In each iteration, the variable indices are split into a "working set":  SYMBOL  and its complement  SYMBOL
Then, the sub-problem with variables  SYMBOL , is solved, thereby, leaving the values of the remaining variables  SYMBOL  unchanged
This method leads to a small sub-problem to be minimized in each iteration
An extreme case is the Sequential Minimal Optimization~(SMO)~ CITATION , which restricts working set to have only two elements
Comparative tests against other algorithms, done by Platt~ CITATION , indicates that SMO is often much faster and has better scaling properties
Since only few components are updated per iteration, for difficult problems, the decomposition method suffers from slow convergence
Better method of working set selection can reduce the number of iterations and hence is an important research issue
Some methods were proposed to solve this problem and to reduce the time of training SVMs~ CITATION
In this paper, %we proposed  we propose a new model to select the working set
In this model, specially, it selects  SYMBOL  without reselection
In another word, once  SYMBOL  are selected, they will not be tested or selected during the following working set selection
Experiments demonstrate that the new model is in general faster than existing methods
This paper is organized as following
In section II, we give literature review, SMO decomposition method and existing working set selection are both discussed
A new method of working set selection is then presented in section III
In section IV, experiments with corresponding analysis are given
Finally, section V concludes this paper
