Conference Paper

An Imperfect String Matching Experience Using Deformed Fuzzy Automata.

Dpt. Automática y Computación, Universidad Pública de Navarra Campus de Arrosadía, 31006, Pamplona, Spain
Conference: Soft Computing Systems - Design, Management and Applications, HIS 2002, December 1-4, 2002, Santiago, Chile
Source: DBLP

ABSTRACT This paper presents a string matching experience using de-formed fuzzy automata for the recognition of imperfect strings. We pro-pose an algorithm based on a deformed fuzzy automaton that calculates a similarity value between strings having a non-limited number of edi-tion errors. Different selections of the fuzzy operators for computing the deformed fuzzy automaton transitions allows to obtain different string similarity definitions. The selection of the parameters determining the deformed fuzzy automaton behavior is obtained via genetic algorithms.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Experiments comparing neural networks trained with crisp and fuzzy desired outputs are described. A handwritten word recognition algorithm using the neural networks for character level confidence assignment was tested on images of words taken from the United States Postal Service mailstream. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level. This empirical result is interpreted as an example of the principle of least commitment
    IEEE Transactions on Fuzzy Systems 09/1995; 3(3-3):357 - 363. DOI:10.1109/91.413223 · 6.31 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The use of diverse knowledge sources in text recognition and in correction of letter substitution errors in words of text is considered. Three knowledge sources are defined: channel characteristics as probabilities that observed letters are corruptions of other letters, bottom-up context as letter conditional probabilities (when the previous letters of the word are known), and top-down context as a lexicon. Two algorithms, one based on integrating the knowledge sources in a single step and the other based on sequentially cascading bottom-up and top-down processes, are compared in terms of computational/storage requirements and results of experimentation.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 04/1983; 5(4):384-95. DOI:10.1109/TPAMI.1983.4767408 · 5.69 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Given two strings X and Y over a finite alphabet, the normalized edit distance between X and Y , d ( X , Y ) is defined as the minimum of W ( P )/ L ( P ), where P is an editing path between X and Y , W ( P ) is the sum of the weights of the elementary edit operations of P , and L ( P ) is the number of these operations (length of P ). It is shown that in general, d ( X , Y ) cannot be computed by first obtaining the conventional (unnormalized) edit distance between X and Y and then normalizing this value by the length of the corresponding editing path. In order to compute normalized edit distances, an algorithm that can be implemented to work in O ( m × n <sup>2</sup>) time and O ( n <sup>2</sup>) memory space is proposed, where m and n are the lengths of the strings under consideration, and m &ges; n . Experiments in hand-written digit recognition are presented, revealing that the normalized edit distance consistently provides better results than both unnormalized or post-normalized classical edit distances
    IEEE Transactions on Pattern Analysis and Machine Intelligence 10/1993; 15(9):926-932. DOI:10.1109/34.232078 · 5.69 Impact Factor

Full-text (4 Sources)

Available from
May 30, 2014