An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators (1992)
| Venue: | Computational Learning Theory and Natural Learning Systems |
| Citations: | 42 - 9 self |
BibTeX
@INPROCEEDINGS{Moore92anempirical,
author = {Andrew W. Moore and Daniel J. Hill and Michael P. Johnson},
title = {An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators},
booktitle = {Computational Learning Theory and Natural Learning Systems},
year = {1992},
publisher = {MIT Press}
}
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Abstract
The generalization error of a function approximator, feature set or smoother can be estimated directly by the leave-one-out cross-validation error. For memory-based methods, this is computationally feasible. We describe an initial version of a general memory-based learning system (GMBL): a large collection of learners brought into a widely applicable machine-learning family. We present ongoing investigations into search algorithms which, given a dataset, find the family members and features that generalize best. We also describe GMBL's application to two noisy, difficult problems---predicting car engine emissions from pressure waves, and controlling a robot billiards player with redundant state variables. 1 Introduction The main engineering benefit of machine learning is its application to autonomous systems in which human decision making is minimized. Function approximation plays a large and successful role in this process. However, many other human decisions are needed even for si...
