Ni, Ji and Rockett, Peter (2015) Training genetic programming classifiers by vicinal-risk minimization. Genetic Programming and Evolvable Machine, 16 (1). pp. 3-25. ISSN 1389-2576
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Item Type: | Article |
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Item Status: | Live Archive |
Abstract
We propose and motivate the use of vicinal-risk minimization (VRM) for training genetic programming classifiers. We demonstrate that VRM has a number of attractive properties and demonstrate that it has a better correlation with
generalization error compared to empirical risk minimization (ERM) so is more likely to lead to better generalization performance, in general. From the results of statistical tests over a range of real and synthetic datasets, we further demonstrate that VRM yields consistently superior generalization errors compared to conventional ERM.
Keywords: | Genetic programming, Classification, Vicinal-risk minimization, JCNotOpen |
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Subjects: | G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G760 Machine Learning G Mathematical and Computer Sciences > G340 Statistical Modelling |
Divisions: | College of Science > School of Computer Science |
Related URLs: | |
ID Code: | 19206 |
Deposited On: | 23 Oct 2015 14:34 |
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