Titlebook: Algorithmic Learning Theory; 4th International Wo Klaus P. Jantke,Shigenobu Kobayashi,Takashi Yokomo Conference proceedings 1993 Springer-V

A保存的

上流社会

https://doi.org/10.1007/978-3-531-90520-4lled competing semantics, we show that the learnability result for positive and negative data can be lifted to the general case of arbitrary patterns. Learning under the standard semantics from positive data is closely related to monotonic language learning.

冰雹

船员

拖债

Das Bologna-Projekt: Entwicklungsphasenr functions, which learning device are trying to identify at the same time, i.e., these classes are identifiable only with learning devices that show some improvement of learning capabilities with practice.

Canyon

Maximizer

Optimal layered learning: A PAC approach to incremental sampling,ize (.) increases exponentially and the error (.) decreases exponentially. As a consequence, a model of layered learning which requires that ., rather than ., be a polynomial function of the logarithm of the concept space would make learnable many concept classes which are not learnable in Valiant‘s PAC model.

Factual

Towards efficient inductive synthesis of expressions from input/output examples,e art of computer experiments using this methodology is described. An example that is considered is the inductive inference of the formula for solving quadratic equations, the finding of which by pure exhaustive search would be unrealistic.

FOVEA

彩色

Generalized unification as background knowledge in learning logic programs,mber of atoms in ., but as the number of symbols in .. This becomes possible because the evaluation of destructors in generalized unification corresponds to the use of background predicates in Džeroski‘s algorithm.