Cumulus
发表于 2025-3-30 10:29:34
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linguistics
发表于 2025-3-30 13:48:32
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王得到
发表于 2025-3-30 20:36:48
Learning from incomplete boundary queries using split graphs and hypergraphs,et al. , 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
发表于 2025-3-30 22:35:22
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GRAVE
发表于 2025-3-31 02:10:41
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
使满足
发表于 2025-3-31 08:35:03
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痛苦一下
发表于 2025-3-31 12:54:37
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Allure
发表于 2025-3-31 13:35:21
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
发表于 2025-3-31 18:36:55
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
拖网
发表于 2025-3-31 22:32:27
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