Efficient SVM Regression Training with SMO

摘要

The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver, In this work, we generalize SMO so that it can handle regression problems. However, one problem with SMO is that its rate of convergence slows down dramtically when data is non-sparse and when there are many support vectors in the solution-as is often the case in regression-because kernel function evaluations tend to dominate the runtime in this case.