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Information-Based Objective Functions for Active Data Selection (0)
| Venue: | Neural Computation |
| Citations: | 426 - 4 self |
BibTeX
@ARTICLE{MacKay_information-basedobjective,
author = {David J.C. MacKay},
title = {Information-Based Objective Functions for Active Data Selection},
journal = {Neural Computation},
year = {},
volume = {4},
pages = {590--604}
}
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Abstract
Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness. 1 Introduction Theories for data modelling often assume that the data is provided by a source that we do not control. However, there are two scenarios in which we are able to actively select training data. In the first, data measurements are relatively expensive or slow, and we want to know where to look next so as to learn as much as possible. According to Jaynes (1986), Bayesian reasoning was first applied to this problem two centuries ago by Laplace, who in consequence made more important discoveries...
Keyphrases
active data selection information-based objective function main weakness bayesian learning framework hypothesis space different criterion candidate measurement data selection expected informativeness select training data data measurement bayesian reasoning important discovery introduction theory alternative specification salient data point objective function
