Titlebook: Algorithmic Learning for Knowledge-Based Systems; GOSLER Final Report Klaus P. Jantke,Steffen Lange Book 1995 Springer-Verlag Berlin Heidel

樸素

Topological considerations in composing teams of learning machines, that any team identifying . contains strategies from all the families. For . the possibility of such splitting depends upon .. The relation between these phenomena and “voting” properties for types ., etc. is revealed.

膽小懦夫

Dissonance

Classifying recursive predicates and languages,ish a new hierarchy. Furthermore, we introduce a formalization of . and characterize it. Finally, we study the classification of families of languages that have attracted a lot of attention in learning theory.

modifier

0302-9743 rticipants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory.978-3-540-60217-0978-3-540-44737-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

搏斗

https://doi.org/10.1007/978-3-658-37123-4eved only when an unbounded number of alternations of these dual types of hypotheses is allowed. Finally, a universal method is presented enabling an inductive inference strategy to verify the incorrectness of any of its incorrect intermediate hypotheses.

WAG

https://doi.org/10.1007/978-3-658-37123-4 numberings..We then show that similar effects can be achieved for learning pattern languages and finite automata from good examples in polynomial time essentially using the “structure” of these objects. Here the number of the good examples is . by the size of the objects to be learnt (length of pattern, number of states, respectively).

savage

https://doi.org/10.1007/978-3-476-03346-8cted model..Part A of the paper shows the basis for different Machine Learning methods using logical equations. Part B gives a way how to use optimal strategies in order to obtain complete knowledge. A few examples illustrate the method.

deficiency

連詞

減去

,Optimal strategies — Learning from examples — Boolean equations,cted model..Part A of the paper shows the basis for different Machine Learning methods using logical equations. Part B gives a way how to use optimal strategies in order to obtain complete knowledge. A few examples illustrate the method.