The Improvement Of The Voting Method For Multi-class SVM Classification

Support Vector Machine is a new machine learning algorithm for classification and regression question. It based on structural risk minimization can effectively solve the over study problem and the good extension and better classified accuracy. It has become new research hotspot after the research of pattern recognition and artificial nerve net and will push development in machine learning.Recently, Support Vector Machine is well applied in pattern recognition, function approximate, data mining and text auto categorization. Traditional Support Vector Machine is developed for binary classification problems, while for practical problems such as data mining and text categorization that need to deal with huge and multi-category data. It is one of the important challenges how to solve large scale and multi-class problems in recent years. Statistical learning theory is introduced in this paper and Support Vector Machine based on this theory is researched. The development history and research issues of Support Vector Machine are expatiated. The dichotomy classification algorithm and multi-class classification methods are summarized, and their advantage, disadvantage and capability are compared.Finally, the disadvantages of the existing methods of Support Vector Machine voting classification are analyzed and compared in this paper. To solve the problems, the minimum internal convex hull algorithm is proposed in this paper. The method holds both low-computational costs and faster calculation speed .It shows that the speeds of training and classifying are improved remarkably and it is a good solution to solve Support Vector Machine Multi-classification problems that have a high request for calculating speed and need to deal with huge data。...