SVM Classification Algorithm And Application Research On Evolution Algorithm

Different from the traditional statistical theory, Statistical Learning Theory targeted at small sample statistics, which not only takes into account the progressive performance requirements, and limited information can be obtained under conditions of optimal structure. Based on this theory, Support Vector Machine algorithm based on VC theory and Structural Risk Minimization principle, based on limited sample of information in the complexity of the model and learning ability to find the best compromise between in order to obtain the best generalization ability.However, as a newer theory of machine learning field, support Vector Machine is still being developed and constant improved. This thesis has a in-depth and meticulous research on SVM classification algorithm (including the kernel function selection, parameter optimization, performance evaluation, etc.). Generally speaking, the main research contents of this article, research results are as follows:1. Have a intensive study on theoretical polynomial kernel and Gaussian RBF kernel function, and from the perspective of Probability and Statistics (to improve the definition of the traditional empirical risk), through the sample's linear dividable degree and linear-intensive Quantitative analysis of the impact on decision-making which nuclear function has imposed on, presents a performance evaluation of nuclear function, and the experimental verifies its algorithm affectivity.2. Under the premise of the classical multi-class classification algorithm research, come up with a algorithm named Improved Binary Decision Tree-based SVM multi-classification, that is IBDT-SVM. Which prune the train-set by means of K-NN algorithm weighted by distance, then carry on SVM multi-category classification by introducing GA to construct optimal or nearest optimal decision binary tree. By experiment, demonstrating algorithm classification accuracy and classification speed certain superiority, in comparison with traditional SVM multi-category classification algorithms.3. Apply multi-class classification algorithm, to solve the problem of the traditional evolution computation can not make use of a priori knowledge of the objective function with similar characteristics, proposed a "Function Cluster Weight Model" in order to address the characteristics quantization problem of function sets.