Continuous Optimization of Hyper-Parameters


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


  • Presentations referencing similar topics