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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
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2.21

Neural Computation

April 1, 2001, Vol. 13, No. 4, Pages 935-957
(doi: 10.1162/089976601300014411)
© 2001 Massachusetts Institute of Technology
A New On-Line Learning Model
Article PDF (239.4 KB)
Abstract

We introduce a new supervised learning model that is a nonhomogeneous Markov process and investigate its properties. We are interested in conditions that ensure that the process converges to a “correct state,” which means that the system agrees with the teacher on every “question.” We prove a sufficient condition for almost sure convergence to a correct state and give several applications to the convergence theorem. In particular, we prove several convergence results for well-known learning rules in neural networks.