Evolvability need not imply learnability

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dc.creatorSrivastava, Nisheeth
dc.date2009-04-03
dc.date.accessioned2026-07-07T13:00:36Z
dc.date.available2026-07-07T13:00:36Z
dc.descriptionWe show that Boolean functions expressible as monotone disjunctive normal forms are PAC-evolvable under a uniform distribution on the Boolean cube if the hypothesis size is allowed to remain fixed. We further show that this result is insufficient to prove the PAC-learnability of monotone Boolean functions, thereby demonstrating a counter-example to a recent claim to the contrary. We further discuss scenarios wherein evolvability and learnability will coincide as well as scenarios under which they differ. The implications of the latter case on the prospects of learning in complex hypothesis spaces is briefly examined.
dc.identifierhttps://arxiv.org/abs/0904.0648
dc.identifierhttp://arxiv.org/abs/0904.0648
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/225890
dc.subjectMachine Learning
dc.subjectComputational Complexity
dc.titleEvolvability need not imply learnability
dc.typetext

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