Oded Maron, Andrew Moore
Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Tech(cid:173) niques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models re(cid:173) duce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly dis(cid:173) carding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leave-one-out cross validation applied to memory(cid:173) based learning algorithms, but we also argue that it is applicable to any class of model selection problems.