Adaptive Optimization for Cross Validation
Alessandro Rudi, Gabriele Chiusano, Alessandro Verri · The European Symposium on Artificial Neural Networks · 2012
The process of model selection and assessment aims at nding a subset of parameters that minimize the expected test error for a model related to a learning algorithm. Given a subset of tuning parameters, an exhaustive grid search is typically performed. In this paper an automatic algorithm for model selection and assessment is proposed. It adaptively learns the error function in the parameters space, making use of the Scale Space theory and the Statistical Learning theory in order to estimate a reduced number of models and, at the same time, to make them more reliable. Extensive experiments are performed on the MNIST dataset.