Titlebook: Computational Learning Theory; Third European Confe Shai Ben-David Conference proceedings 1997 Springer-Verlag Berlin Heidelberg 1997 Algor

Cumulus

linguistics

王得到

Learning from incomplete boundary queries using split graphs and hypergraphs,et al. [7], it is assumed that membership queries on instances near the boundary of the target concept may receive a “don‘t know” answer..We show that zero-one threshold functions are efficiently learnable in this model. The learning algorithm uses split graphs when the boundary region has radius 1,

gerrymander

GRAVE

Monotonic and dual-monotonic probabilistic language learning of indexed families with high probabilive data. In particular, we consider the special case where the probability is equal to 1..Earlier results in the field of probabilistic identification established that — considering function identification — each collection of recursive functions identifiable with probability .>1/2 is deterministic

使?jié)M足

痛苦一下

Allure

Learning under persistent drift,re the changes might be rapid but their “direction” is relatively constant. We model this type of change by assuming that the target distribution is changing continuously at a constant rate from one extreme distribution to another. We show in this case how to use a simple weighting scheme to estimat

arthroplasty

Randomized hypotheses and minimum disagreement hypotheses for learning with noise,andomized hypotheses for learning with small sample sizes and high malicious noise rates. We show an algorithm that PAC learns any target class of VC-dimension . using randomized hypotheses and order of . training examples (up to logarithmic factors) while tolerating malicious noise rates even sligh

拖網(wǎng)

Learning when to trust which experts,hat this assumption does not take advantage of situations where both the outcome and the experts‘ predictions are based on some input which the learner gets to observe too. In particular, we exhibit a situation where each individual expert performs badly but collectively they perform well, and show