Titlebook: Algorithmic Learning Theory; 6th International Wo Klaus P. Jantke,Takeshi Shinohara,Thomas Zeugmann Conference proceedings 1995 Springer-Ve

使成核

COST

Simulating teams with many conjectures,learning paradigm, a learner is given examples from a recursive function, which the learner attempts to learn by producing programs to compute that function. We say that a team is successful if at least one member of the team learns the target function. The problem, given two teams with bounded numb

功多汁水

被告

Learning ordered binary decision diagrams,ueries that finds the minimum obdd for the target respecting a given ordering. We also prove that both types of queries and the restriction to a given ordering are necessary if we want minimality in the output, unless P=NP. If learning has to occur with respect to the optimal variable ordering, poly

obligation

Simple PAC learning of simple decision lists, PAG learning model. The proof is based on a transformation from an algorithm based on equivalence queries (found independently by Simon). Then we introduce the class of simple decision lists, and extend our algorithm to show that simple decision lists are simple-PAC learnable as well. This last res

漂泊

The complexity of learning minor closed graph classes,olynomial time using membership and equivalence queries. The representation of the class is in terms of a set of minimal excluded minors (obstruction set). Moreover, a negative result for learning such classes using only equivalence queries is also provided, after introducing a notion of reducibilit

致词

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/152965.jpg

seruting

鞠躬

https://doi.org/10.1007/978-3-322-92171-0 by first learning an initial segment, .., of the infinite state machine that accepts the unknown language and then decomposing it into a complete control structure and a partial counter. A new, efficient ROCA decomposition algorithm, which will be presented in detail, allows this result. The decomp

沙发

Bankpolitik in traditioneller Sichtnguage generated by an . even linear grammar. We introduce a natural subclass of . even linear languages, called ., and show that this subclass is learnable in the limit from positive examples. Furthermore, we propose a learning algorithm that identifies this subclass in the limit with almost linear