A new on-line learning model.
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.
Mendelson
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Department of Mathematics, Technion, Haifa 32000, Israel.
D001185:Artificial Intelligence; D007858:Learning; D008390:Markov Chains; D008959:Models, Neurological; D009415:Nerve Net; D013696:Temperature