An adaptive finite-state automata application to the problem of reducing the number of states in approximate string matching

This paper presents an alternative way to use finite-state automata in order to deal with approximate string matching. By exploring some adaptive features that enable any finitestate automaton model to change configuration during computational steps, dynamically deleting or creating new transitions, we can actually control the behavior and the topology of the automaton. We use these features for an application to approximate string matching trying to reduce the number of states requiredEje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

An adaptive finite-state automata application to the problem of reducing the number of states in approximate string matching

This paper presents an alternative way to use finite-state automata in order to deal with approximate string matching. By exploring some adaptive features that enable any finitestate automaton model to change configuration during computational steps, dynamically deleting or creating new transitions, we can actually control the behavior and the topology of the automaton. We use these features for an application to approximate string matching trying to reduce the number of states requiredEje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Computation of distances for regular and context-free probabilistic languages

Several mathematical distances between probabilistic languages have been investigated in the literature, motivated by applications in language modeling, computational biology, syntactic pattern matching and machine learning. In most cases, only pairs of probabilistic regular languages were considered. In this paper we extend the previous results to pairs of languages generated by a probabilistic context-free grammar and a probabilistic finite automaton.PostprintPeer reviewe

Fast and Compact Regular Expression Matching

We study 4 problems in string matching, namely, regular expression matching, approximate regular expression matching, string edit distance, and subsequence indexing, on a standard word RAM model of computation that allows logarithmic-sized words to be manipulated in constant time. We show how to improve the space and/or remove a dependency on the alphabet size for each problem using either an improved tabulation technique of an existing algorithm or by combining known algorithms in a new way

Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts

We study the approximate string matching and regular expression matching problem for the case when the text to be searched is compressed with the Ziv-Lempel adaptive dictionary compression schemes. We present a time-space trade-off that leads to algorithms improving the previously known complexities for both problems. In particular, we significantly improve the space bounds, which in practical applications are likely to be a bottleneck