A Tutorial Survey of Support Vector Machine Training Algorithms
JI Wang-tian · Microcomputer Development · 2004
Training SVM can be formulated into a quadratic programming problem. For large learning tasks with many training examples, off-the-shelf optimization techniques quickly become intractable in their memory and time requirements. Thus, many efficient techniques have been developed. These techniques divide the original problem into several smaller sub-problems. By solving these sub-problems iteratively, the original larger problem is solved. All proposed methods suffer from the bottleneck of long training time. This severely limited the widespread application of SVM. This paper systematically surveyed three mainstream SVM training algorithms: chunking, decomposition, and sequential minimal optimization algorithms. It concludes with an illustration of future directions.