Yet another zeta function and learning

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dc.creatorRivin, Igor
dc.date2001-07-25
dc.date.accessioned2026-07-07T03:17:23Z
dc.date.available2026-07-07T03:17:23Z
dc.descriptionWe study the convergence speed of the batch learning algorithm, and compare its speed to that of the memoryless learning algorithm and of learning with memory (as analyzed in joint work with N. Komarova). We obtain precise results and show in particular that the batch learning algorithm is never worse than the memoryless learning algorithm (at least asymptotically). Its performance vis-a-vis learning with full memory is less clearcut, and depends on certainprobabilistic assumptions. These results necessitate theintroduction of the moment zeta function of a probability distribution and the study of some of its properties.
dc.identifierhttps://arxiv.org/abs/cs/0107033
dc.identifierhttp://arxiv.org/abs/cs/0107033
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30703
dc.subjectMachine Learning
dc.subjectDiscrete Mathematics
dc.subjectProbability
dc.subjectI.2.6; G.3
dc.titleYet another zeta function and learning
dc.typetext

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