Adaptive Kernel Value Caching for SVM Training.

Support vector machines (SVMs) can solve structured multioutput learning problems such as multilabel classification, multiclass classification, and vector regression. SVM training is expensive, especially for large and high-dimensional data sets. The bottleneck of the SVM training often lies in the kernel value computation.