Research And Applications Of Support Vector Machines

Support Vector Machine (SVM) is a kind of novel machine learning methods, theoretically based on statistic learning theory. It employs the criteria of structural risk minimization. And it's a quadratic programming problem which can make sure that the extreme solution found is the optimal one. So it can use limited information to obtain statistic principles and high generalization, and can also provide a framework for the small samples, nonlinearity and high dimension problems which most traditional learning methods can't solve. In this paper, a series of work on the theory and application of support vector machine was discussed.After analyzing the SVM theory in detail and using the characteristic of solution sparseness, a data reducing algorithm of support vector machine based on fuzzy kernel clustering was proposed. Through nonlinear mapping and kernel trick, the data which were mapped into a high dimensional feature space from the original space can cluster in the feature space by using fuzzy kernel clustering algorithm. So the data which were most likely to be support vectors, can be found from the sub-clusters that were located near the optimal classification hyperplane. And the size of sample-data for SVM training turns to be small. Meanwhile the training time was reduced greatly without compromising the generalization capability. The simulations show that this new method was effective.In order to further improve the generalization of SVM, an improved support vector regression ensemble algorithm was proposed. Learning by a series of support vector regressions and combining all the results in accordance with some rule, the algorithm improves its regression performance greatly. Moreover, the proposed algorithm was used in a soft–sensor model for the Bisphenol-A productive process. Simulations using artificial and real data also demonstrated that the algorithm was effective.Parameter selection was one of the most important issues in the research of support vector machines. The previous researches show that the SVM's generalization capacity was greatly affected by its parameters. But there have been few theoretical methods to choose the SVR's parameters so far. A solution path algorithm with respect to kernel parameter based on the bisection method was proposed. With the update of the parameter, the current solution can be computed based on an already obtained one, and the value of the parameter which is correlated with the extreme value of the target function is the optimal one. Simulations using artificial and real data show that this algorithm can quickly get the model which has better generalization.