Fast model selection for MaxMinOver-based training of support vector machines

OneClassMaxMinOver (OMMO) is a simple incremental algorithm for one-class support vector classification. We propose several enhancements and heuristics for improving model selection, including the adaptation of well-known techniques such as kernel caching and the evaluation of the feasibility gap. Furthermore, we provide a framework for optimising grid search based model selection that compromises of preinitialisation, cache reuse, and optimal path selection. Finally, we derive simple heuristics for choosing the optimal grid search path based on common benchmark datasets. In total, the proposed modifications improve the runtime of model selection significantly while they are still simple and adaptable to a wide range of incremental support vector algorithms.