Studies On Algorithm Of Support Vector Machine

With the development of computer and informational technology, more and more price need to be paid for collecting, storing and processing vast data. Lots of databases are produced for business, administration, science, engineering and so on which natural gave birth to data mining technique. KDD is a process searching for useful, potential and understandable form from data. It intercrosses a lot of subjects and technologies such as machine learning, mathematical programming, statistics, pattern recognition and so on.Mathematical programming is an important branch of operational research. Its applications can be seen in many areas, such as machine learning, networks problem, mechanics.Especially, combining it with data mining makes it possible to solve large-scale and complicated problems and it has also been successfully applied to feature selection, clustering and regression.Support vector machine is one of the important results of applying mathematical programming to data mining and it is a machine learning method that was brought out by V.Vapnik according to statistic theory.Unconstrained models with 1 -norm or 2-norm cost functions are the focuses of the paper. Nonsmooth technique is employed to the 1-norm case, and variational metric method to 2-norm case. For nonlinear case, Generalized SVM is introduced which makes previous result still hold. Both methods are implemented by Matlab and satisfactory results are obtained.