Continuous Optimization of Hyper-Parameters

Abstract

Many machine learning algorithms can be formulated as the minimization of a train ing criterion which involves training errors on each training example and some hyper parameters which are kept xed during this minimization When there is only a single hyper parameter one can easily explore how its value a ects a model selection criterion that is not the same… (More)
DOI: 10.1109/IJCNN.2000.857853

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