Finding optimal model parameters by discrete grid search

Abstract

Finding optimal parameters for a model is usually a crucial task in engineering approaches to classification and modeling tasks. An automated approach is particularly desirable when a hybrid approach combining several distinct methods is to be used. In this work we present an algorithm for finding optimal parameters that works with no specific information about the underlying model and only requires the discretization of the parameter range to be considered. We will illustrate the procedure's performance for multilayer perceptrons and support vector machines, obtaining competitive results with state-of-the-art procedures whose parameters have been tuned by experts. Our procedure is much more efficient than straight parameter search (and probably than other procedures that have appeared in the literature), but it may nevertheless require extensive computations to arrive at the best parameter values, a potential drawback that can be overcome in practice because of its highly parallelizable nature. © 2007 Springer-Verlag Berlin Heidelberg.