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

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 p...

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Published in:	Journal of intelligent information systems Vol. 31; no. 2; pp. 177 - 189
Main Authors:	Cornuéjols, Antoine, Sebag, Michèle
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.10.2008 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Bioinformatics Computer Science Data Structures and Information Theory Grammar Heuristic Hypotheses Information Storage and Retrieval IT in Business Language Logic programming Natural Language Processing (NLP) Phase transitions Studies Supervised learning Sequence learning Grammatical inference
ISSN:	0925-9902, 1573-7675
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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.
AbstractList	Issue Title: Special issue for ISIP 2005; Guest editors: Mohand-Said Hacid, Nicolas Spyratos, and Yuzuru Tanaka 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. [PUBLICATION 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.
Author	Sebag, Michèle Cornuéjols, Antoine
Author_xml	– sequence: 1 givenname: Antoine surname: Cornuéjols fullname: Cornuéjols, Antoine email: antoine@lri.fr, antoine.cornuejols@agroparistech.fr organization: AgroParisTech / INRA UMR 518, Mathématiques et Informatique Appliquées – sequence: 2 givenname: Michèle surname: Sebag fullname: Sebag, Michèle organization: Laboratoire de Recherche en Informatique, CNRS UMR 8623, Bât.490, Université de Paris-Sud
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Title	A note on phase transitions and computational pitfalls of learning from sequences
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