Titlebook: Computational Learning Theory; 15th Annual Conferen Jyrki Kivinen,Robert H. Sloan Conference proceedings 2002 Springer-Verlag Berlin Heidel

寒冷

Inferring Deterministic Linear Languagesministic linear grammars, and for a reasonable definition prove the existence of a canonical normal form. This enables us to obtain positive learning results in case of polynomial learning from a given set of both positive and negative examples. The resulting class is the largest one for which this

AUGUR

seroma

The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictionsn .(.). Instead of using the unknown .(.) he predicts using the celebrated universal enumerable prior .(.) which for all . exceeds any recursive .(.), save for a constant factor independent of .. The simplicity measure .(.) naturally implements “Occam’s razor” and is closely related to the Kolmogoro

向下五度才偏

aplomb

Exploring Learnability between Exact and PACxamples to equivalence queries are distributionally drawn rather than adversarially chosen or as the Probably Approximately Correct (PAC) model strengthened to require a perfect hypothesis. We also introduce a model of Probably Almost Exactly Correct (PAExact) learning that requires a hypothesis wit

interrogate

Tempor

Fatten

Agnostic Learning Nonconvex Function Classesre positively, we show one can obtain “fast” sample complexity bounds for nonconvex . for “most” target conditional expectations. The new bounds depend on the detailed geometry of ., in particular the distance in a certain sense of the target’s conditional expectation from the set of nonuniqueness points of the class ..

agnostic

Learning Tree Languages from Texte time and prove its correctness. In this way, we generalize previous works of Angluin, Sakakibara and ourselves. Moreover, we show that this way all regular tree languages can be identified approximately.

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