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Learning with ordinal-bounded memory from positive data

โœ Scribed by Lorenzo Carlucci; Sanjay Jain; Frank Stephan


Book ID
119292547
Publisher
Elsevier Science
Year
2012
Tongue
English
Weight
267 KB
Volume
78
Category
Article
ISSN
0022-0000

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๐Ÿ“œ SIMILAR VOLUMES


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Learning with bounded memory in stochastic frameworks is incomplete in the sense that the learning dynamics cannot converge to a rational expectations equilibrium (REE). The properties of dynamics arising from such rules are studied for standard models with steady states. If the REE in linear models

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The present paper deals with a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every infinite sequence of elements exhausting c is called positive presentation of c. An algorithmic learner successively takes as input one element of