NDLI: A note on phase transitions and computational pitfalls of learning from sequences

Content Provider	Springer Nature Link
Author	Cornuéjols, Antoine Sebag, Michèle
Copyright Year	2008
Abstract	An ever greater range of applications call for learning from sequences. Grammar induction is one prominent tool for sequence learning, it is therefore important to know its properties and limits. This paper presents a new type of analysis for inductive learning. A few years ago, the discovery of a phase transition phenomenon in inductive logic programming proved that fundamental characteristics of the learning problems may affect the very possibility of learning under very general conditions. We show that, in the case of grammatical inference, while there is no phase transition when considering the whole hypothesis space, there is a much more severe “gap” phenomenon affecting the effective search space of standard grammatical induction algorithms for deterministic finite automata (DFA). Focusing on standard search heuristics, we show that they overcome this difficulty to some extent, but that they are subject to overgeneralization. The paper last suggests some directions to alleviate this problem.
Starting Page	177
Ending Page	189
Page Count	13
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	31
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2008-05-23
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Grammatical inference Sequence learning Supervised learning Business Information Systems Document Preparation and Text Processing Artificial Intelligence (incl. Robotics) Data Structures, Cryptology and Information Theory
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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