Pattern Recognition Algorithm Based On Support Vector Machine

Support vector machine is a pattern recognition algorithm based on statistical learning theory. Being substituted structural risk minimization for empirical risk minimization, support vector machine solves some problems puzzling pattern recognition field within a long period. Support vector machine has a good ability processing fewer simples. A nonlinear problem can be transformed to a linear problem with using kernel functions. The transformation reduces complexity of the algorithm. With some highlights such as perfect theories, support vector machine is a hot point in pattern recognition field nowadays.Theoretical basis for SVM-related statistical learning theory and concepts are introduced at the beginning. Some analysis of 2-category SVM algorithms performance and a summary of their advantages and disadvantages are done in chapter 2. Realization of multi-category classification algorithms be researched and analyzed in the next chapter. Some comparisons of their features are done in the chapter.For massive training sets, an incremental learning method for SVM is proposed in chapter 4. Such algorithms through analysis Support Vectors distribution characteristics use small-scale matrix operations to replace large-scale matrix operations. Experimental results of fabric blemish detection show that the algorithm effectively improves training speed.