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A proxy learning curve for the Bayes classifier
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DOI：10.1016/j.patcog.2022.109240.png)
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摘要
En 中文
In this paper, a theoretical learning curve is derived for the multi-class Bayes classifier. This curve fits general multivariate parametric models of the class-conditional probability density. The derivation uses a proxy approach based on analyzing the convergence of a statistic which is proportional to the posterior probability of the true class. By doing so, the curve depends only on the training set size and on the dimension of the feature vector; it does not depend on the model parameters. Essentially, the learning curve provides an estimate of the reduction in the excess of the probability of error that can be obtained by increasing the training set size. This makes it attractive in order to deal with the practical problems of defining appropriate training set sizes.(c) 2022 The Author(s). Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keyword:
Classification
Parameter learning
Sample size
Training set size
Probability of error
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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