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In the field of machine learning, support vector machine is a supervised learning model which can analyze the data and identify patterns, and this theory is used for classification and regression analysis which is related to the learning algorithm. Support vector machine is a new machine learning method based on statistical learning theory, and has become a hot research topic in the machine learning field because of its excellent learning performance. Support vector machine is also a learning machine based on kernel function, and its generalization ability depends on the chosen kernel function to a great extent. Nevertheless, traditional support vector machine cannot achieve the desired results when training large-scale data set. Therefore, in order to improve training efficiency and generalization performance of support vector machine, we have to improve this algorithm. Although support vector machine has great advantages in theory, the research on its application is comparatively delayed. In this paper, we focus on novel support vector machine generated from multi-subjects amalgamation, such as fuzzy support vector machine, granular support vector machine, twin support vector machine. We analyze a large number of literature review about development trend of support vector machine and its applications in computer field, laying foundation for the systematic research on the correlation algorithm based on support vector machine.

Development Trend of Support Vector Machine and Applications on the Field of Computer Science