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Titlebook: Algorithmic Learning Theory; 6th International Wo Klaus P. Jantke,Takeshi Shinohara,Thomas Zeugmann Conference proceedings 1995 Springer-Ve

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41#
發(fā)表于 2025-3-28 15:53:25 | 只看該作者
42#
發(fā)表于 2025-3-28 19:31:40 | 只看該作者
43#
發(fā)表于 2025-3-29 02:11:09 | 只看該作者
44#
發(fā)表于 2025-3-29 05:30:16 | 只看該作者
Gründung und Errichtung der Kreditinstituteormulas is learnable with membership, equivalence and subset queries. Moreover, it is shown that under some condition the class of orthogonal .-Horn formulas is learnable with membership and equivalence queries.
45#
發(fā)表于 2025-3-29 10:34:49 | 只看該作者
46#
發(fā)表于 2025-3-29 14:35:27 | 只看該作者
?Bankbetrieb“ und ?Bankbetriebslehre“above, we obtain probabilistic hierarchies highly structured without a “gap” between the probabilistic and deterministic learning classes. In the case of exact probabilistic learning, we are able to show the probabilistic hierarchy to be dense for every mentioned monotonicity condition. Considering
47#
發(fā)表于 2025-3-29 18:50:55 | 只看該作者
Learning unions of tree patterns using queries,time PAC-learnability and the polynomial time predictability of .. when membership queries are available. We also show a lower bound . of the number of queries necessary to learn .. using both types of queries. Further, we show that neither types of queries can be eliminated to achieve efficient lea
48#
發(fā)表于 2025-3-29 23:31:36 | 只看該作者
49#
發(fā)表于 2025-3-30 00:29:20 | 只看該作者
Machine induction without revolutionary paradigm shifts,nference, it is shown that there are classes learnable . the non-revolutionary constraint (respectively, with severe parsimony), up to (i}+1) mind changes, and no anomalies, which classes cannot be learned with no size constraint, an unbounded, finite number of anomalies in the final program, but wi
50#
發(fā)表于 2025-3-30 06:32:32 | 只看該作者
Probabilistic language learning under monotonicity constraints,above, we obtain probabilistic hierarchies highly structured without a “gap” between the probabilistic and deterministic learning classes. In the case of exact probabilistic learning, we are able to show the probabilistic hierarchy to be dense for every mentioned monotonicity condition. Considering
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