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dc.contributor.authorFreivalds, Rusins
dc.contributor.authorBarzdins, Janis
dc.contributor.authorPodnieks, Karlis
dc.date.accessioned2013-08-03T06:33:59Z
dc.date.available2013-08-04T00:00:03Z
dc.date.issued1991
dc.identifier.citationR.Freivalds, J.Barzdins, K.Podnieks. Inductive inference of recursive functions: complexity bounds. Lecture Notes in Computer Science, 502, Springer-Verlag, 1991, pp. 111-155en_US
dc.identifier.isbn3-540-54131-4
dc.identifier.urihttps://dspace.lu.lv/dspace/handle/7/1467
dc.description.abstractThis survey includes principal results on complexity of inductive inference for recursively enumerable classes of total recursive functions. Inductive inference is a process to find an algorithm from sample computations. In the case when the given class of functions is recursively enumerable it is easy to define a natural complexity measure for the inductive inference, namely, the worst-case mindchange number for the first n functions in the given class. Surely, the complexity depends not only on the class, but also on the numbering, i.e. which function is the first, which one is the second, etc. It turns out that, if the result of inference is Goedel number, then complexity of inference may vary between log n+o(log2n ) and an arbitrarily slow recursive function. If the result of the inference is an index in the numbering of the recursively enumerable class, then the complexity may go up to const-n. Additionally, effects previously found in the Kolmogorov complexity theory are discovered in the complexity of inductive inference as well.en_US
dc.language.isoengen_US
dc.publisherSpringer Verlagen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectinductive inferenceen_US
dc.subjectcomplexity boundsen_US
dc.subjectcomplexityen_US
dc.subjectdeterministicen_US
dc.subjectprobabilisticen_US
dc.subjectpredictionen_US
dc.titleInductive inference of recursive functions: complexity boundsen_US
dc.typeArticleen_US


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