Monthly
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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 1993, Vol. 5, No. 6, Pages 893-909
(doi: 10.1162/neco.1993.5.6.893)
© 1993 Massachusetts Institute of Technology
Local Algorithms for Pattern Recognition and Dependencies Estimation
Article PDF (661.92 KB)
Abstract

In previous publications (Bottou and Vapnik 1992; Vapnik 1992) we described local learning algorithms, which result in performance improvements for real problems. We present here the theoretical framework on which these algorithms are based. First, we present a new statement of certain learning problems, namely the local risk minimization. We review the basic results of the uniform convergence theory of learning, and extend these results to local risk minimization. We also extend the structural risk minimization principle for both pattern recognition problems and regression problems. This extended induction principle is the basis for a new class of algorithms.