Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation (1994)
Tags
Active Learning, Efficient Statistical Algorithms, Locally Weighted Learning, Memory-based Learning, Optimization
Abstract
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. Techniques 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 reduce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly discarding 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 memorybased learning algorithms, but we also argue that it is applicable to any class of model selection problems.
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Approximate BibTeX Entry
@inproceedings{maron-hoeffding,
Month = {April},
Year = {1994},
Volume = {6},
Pages = {59-66},
Publisher = {Morgan Kaufmann},
Address = {340 Pine Street, 6th Fl., San Francisco, CA 94104},
Booktitle = {Advances in Neural Information Processing Systems},
Editor = {Jack D. Cowan, G. Tesauro & J. Alspector},
Author = {Oded Maron Andrew Moore},
Title = {Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation}
}