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

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樓主: Randomized
51#
發(fā)表于 2025-3-30 10:42:59 | 只看該作者
52#
發(fā)表于 2025-3-30 13:13:54 | 只看該作者
53#
發(fā)表于 2025-3-30 17:48:44 | 只看該作者
54#
發(fā)表于 2025-3-30 22:21:17 | 只看該作者
https://doi.org/10.1007/978-3-531-90520-4lly learns a collection of functions. In this work, we consider the perspective of starting with a set of functions, and considering the collection of learning algorithms that are successful at learning the given functions. Some strong dualities are revealed.
55#
發(fā)表于 2025-3-31 01:24:53 | 只看該作者
56#
發(fā)表于 2025-3-31 07:02:17 | 只看該作者
Das Bologna-Projekt: Entwicklungsphasender the learning of total recursive functions by some algorithmic device (inductive inference machine) and describe the “quality” of learning in two different ways: as probability with which machine identifies the given function correctly, and as density of a set of arguments for which the hypothesi
57#
發(fā)表于 2025-3-31 11:44:37 | 只看該作者
Der Bologna-Prozess als Politiknetzwerk corresponding types of redundancy for Popperian FINite learning. The basis of our analytical framework is a reduction technique used previously by us..Using our analytical tools we determine the redundancy types for all ratios of the form 4., where .≥2, which defines the sequence of capabilities fo
58#
發(fā)表于 2025-3-31 16:25:46 | 只看該作者
59#
發(fā)表于 2025-3-31 21:05:58 | 只看該作者
Der Bologna-Prozess als Politiknetzwerkfocused on trying to understand how the language learning ability of an inductive inference machine is affected when it is constrained. For example, derived from work in inductive logic, notions of monotonicity have been studied which variously reflect the requirement that the learner‘s guess must m
60#
發(fā)表于 2025-3-31 23:46:33 | 只看該作者
https://doi.org/10.1007/978-3-8350-9143-6, that certain families of non-trivial and infinite regular languages can be described with finite tuples of finite sets of strings (or trees). It follows from this property, that the methods are able to inductively learn in the limit from positive samples only. It is also shown that if the mapping
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