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21#
發(fā)表于 2025-3-25 13:16:13 | 只看該作者
Katja Kanzler,Brigitte Georgi-Findlayree languages of arbitrarily many dimensions. Via the correspondence between trees and string languages (“yield operation”) this is equivalent to the statement that this way even some string language classes beyond context-freeness have become learnable with respect to Angluin’s learning model as well.
22#
發(fā)表于 2025-3-25 19:52:07 | 只看該作者
,Musterl?sungen zu den übungen,to extract some nontrivial structure in the form of a PDFA with 30-50 states. An additional feature, in fact partly explaining the reduction in sample size, is that our algorithm does not need as input any information about the distinguishability of the target.
23#
發(fā)表于 2025-3-25 21:27:18 | 只看該作者
A Polynomial Algorithm for the Inference of Context Free Languages is based on a generalisation of distributional learning and uses the lattice of context occurrences. The formalism and the algorithm seem well suited to natural language and in particular to the modelling of first language acquisition.
24#
發(fā)表于 2025-3-26 01:03:20 | 只看該作者
25#
發(fā)表于 2025-3-26 07:29:13 | 只看該作者
26#
發(fā)表于 2025-3-26 10:23:04 | 只看該作者
27#
發(fā)表于 2025-3-26 13:50:27 | 只看該作者
Towards Feasible PAC-Learning of Probabilistic Deterministic Finite Automatato extract some nontrivial structure in the form of a PDFA with 30-50 states. An additional feature, in fact partly explaining the reduction in sample size, is that our algorithm does not need as input any information about the distinguishability of the target.
28#
發(fā)表于 2025-3-26 17:18:50 | 只看該作者
Learning Commutative Regular Languagesmmutative regular languages from positive and negative samples, and we show, from experimental results, that far from being a theoretical algorithm, it produces very high recognition rates in comparison with classical inference algorithms.
29#
發(fā)表于 2025-3-26 23:07:39 | 只看該作者
30#
發(fā)表于 2025-3-27 01:36:15 | 只看該作者
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