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

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书目名称Algorithmic Learning Theory影响因子(影响力)




书目名称Algorithmic Learning Theory影响因子(影响力)学科排名




书目名称Algorithmic Learning Theory网络公开度




书目名称Algorithmic Learning Theory网络公开度学科排名




书目名称Algorithmic Learning Theory被引频次




书目名称Algorithmic Learning Theory被引频次学科排名




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书目名称Algorithmic Learning Theory年度引用学科排名




书目名称Algorithmic Learning Theory读者反馈




书目名称Algorithmic Learning Theory读者反馈学科排名

Tailor

Efficient learning of real time one-counter automata, 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

Vital-Signs

Champion

Language learning from membership queries and characteristic examples,istic example of a language . is an element of . which includes, in a sense, sufficient information to represent .. Every context-free language can be divided into a finite number of languages each of which has a characteristic example and it is decidable whether or not a context-free language has a

倾听

Learning unions of tree patterns using queries,he union of the languages defined by each first-order terms in the set. Unfortunately, the class .. not polynomial time learnable in most of learning frameworks under standard assumptions in computational complexity theory. To overcome this computational hardness, we relax the learning problem by al

不开心

Inductive constraint logic,systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is

陈旧

Incremental learning of logic programs,using the already defined predicates as background knowledge. Our class properly contains the class of innermost simple programs of [20] and the class of hereditary programs of [12,13]. Standard programs for multiplication, quick-sort, reverse and merge are a few examples of programs that can be han

踉跄

Learning orthogonal ,-Horn formulas,le. Recently, it was pointed out that the problem of PAC-learning for these classes with membership queries can be reduced to that of query learning for the class of .-quasi Horn formulas with membership and equivalence queries. A .-quasi Horn formula is a CNF formula with each clause containing at

压碎

Machine induction without revolutionary paradigm shifts,l approaches to forbidding large changes in the size of programs conjectured..One approach, called ., requires all the programs conjectured on the way to success to be nearly (i.e., within a recursive function of) minimal size. It is shown that this very conservative constraint allows learning infin

是限制