A stochastic gradient descent algorithm for structural risk minimisation

Joel Ratsaby

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Structural risk minimisation (SRM) is a general complexity regularization method which automatically selects the model complexity that approximately minimises the misclassification error probability of the empirical risk minimiser. It does so by adding a complexity penalty term ∊(m, k) to the empirical risk of the candidate hypotheses and then for any fixed sample size m it minimises the sum with respect to the model complexity variable k. When learning multicategory classification there are M subsamples mi, corresponding to the M pattern classes with a priori probabilities pi, 1 ≤ i ≤ M. Using the usual representation for a multi-category classifier as M individual boolean classifiers, the penalty becomes ∑Mi=1 pi∊(mi, ki). If the mi are given then the standard SRM trivially applies here by minimizing the penalised empirical risk with respect to ki, 1,..., M. However, in situations where the total sample size ∑Mi=1 mi needs to be minimal one needs to also minimize the penalised empirical risk with respect to the variables mi, i = 1,..., M. The obvious problem is that the empirical risk can only be defined after the subsamples (and hence their sizes) are given (known). Utilising an on-line stochastic gradient descent approach, this paper overcomes this difficulty and introduces a sample-querying algorithm which extends the standard SRM principle. It minimises the penalised empirical risk not only with respect to the ki, as the standard SRM does, but also with respect to the mi, i = 1,..., M. The challenge here is in defining a stochastic empirical criterion which when minimised yields a sequence of subsample-size vectors which asymptotically achieve the Bayes’ optimal error convergence rate.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 14th International Conference, ALT 2003, Proceedings
EditorsRicard Gavalda, Klaus P. Jantke, Eiji Takimoto
PublisherSpringer Verlag
Pages205-220
Number of pages16
ISBN (Print)3540202919, 9783540202912
DOIs
StatePublished - 1 Jan 2003
Externally publishedYes
Event14th International Conference on Algorithmic Learning Theory, ALT 2003 - Sapporo, Japan
Duration: 17 Oct 200319 Oct 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2842
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Algorithmic Learning Theory, ALT 2003
Country/TerritoryJapan
CitySapporo
Period17/10/0319/10/03

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