Adaptive Optimization for Cross Validation
نویسندگان
چکیده
The process of model selection and assessment aims at finding 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.
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