Learning Regular Expressions from Noisy Sequences

Abstract. The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this problem, a learning algorithm based straction mechanism is proposed: it can infer the general model of complex events from a set of learning sequences. Events are described of regular expressions, and the abstraction mechanism is based on the substitution property of regular languages. The induction algorithm proceeds bottom-up, progressively coarsening the sequence granularity, letting correlations between subsequences, separated by long gaps, y emerge. Two abstraction operators are defined. The first one and abstracts into non-terminal symbols, regular expressions not containing iterative constructs. The second one detects and abstracts iterated subsequences. By interleaving the two operators, regular expressions in general form may be inferred. Both operators are based on string alignment algorithms taken from...

• Type: Conference
• Year: 2005
• Where: SARA
• Authors: Ugo Galassi, Attilio Giordana
• Keywords: Artificial Intelligence, Complex Events, Long Gaps, Regular Expressions, SARA 2005