Model selection and error estimation
Bartlett, P. L., Boucheron, S. , & Lugosi, G. (2002) Model selection and error estimation. Machine Learning, 48(1-3), pp. 85-113.
We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.
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|Item Type:||Journal Article|
|Keywords:||Concentration inequalities, Empirical penalties, Model selection, Penalization, Calculations, Computational complexity, Error analysis, Estimation, Integration, Mathematical models, Monte Carlo methods, Pattern recognition, Error estimation, Learning systems|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > Mathematical Sciences
|Deposited On:||12 Aug 2011 12:46|
|Last Modified:||12 Aug 2011 12:46|
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