Incremental learning proximal support vector machine classifiers
Kai Li, Houkuan Huang · 2003
Support vector machines (SVMs) have played a key role in broad classes of problems in various fields. However, with increasing amounts of data being generated by businesses and researchers, SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. The training process involves the solution of a quadratic programming problem. We attempt to overcome these limitations and propose an approach based on an incremental learning technique and a multiple proximal support vector machine classifier. An experiment on a generated data set gives promising results.