(Enter summary)
Abstract: I describe an exploration criterion that attempts to minimize the error of a learner by minimizing its
estimated squared bias. I describe experiments with locally-weighted regression on two simple kinematics
problems, and observe that this "bias-only" approach outperforms the more common "variance-only"
exploration approach, even in the presence of noise.
Copyright c fl Massachusetts Institute of Technology, 1995
This report describes research done at the Center for Biological and Computational ... (Update)
Context of citations to this paper: More
...includes an unknown controlling parameter of the trade o# between the bias and variance, so the optimal solution can not be obtained. Cohn (1997) used resampling methods such as the bootstrapping (Efron Tibshirani, 1993) and the cross validation (Stone, 1974) for estimating...
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BibTeX entry: (Update)
Cohn, D. (1995). Minimizing statistical bias with queries. AI Lab memo AIM1552, Massachusetts Institute of Technology. Available by anonymous ftp from publications.ai.mit.edu. http://citeseer.ist.psu.edu/article/cohn95minimizing.html More
@inproceedings{ cohn97minimizing,
author = "David A. Cohn",
title = "Minimizing Statistical Bias with Queries",
booktitle = "Advances in Neural Information Processing Systems",
volume = "9",
publisher = "The {MIT} Press",
editor = "Michael C. Mozer and Michael I. Jordan and Thomas Petsche",
pages = "417",
year = "1997",
url = "citeseer.ist.psu.edu/article/cohn95minimizing.html" }
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