An incremental learning algorithm based on Support Vector Machine for pattern recognition

With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector Machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. This article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. This article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. The experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process.