An evolutionary approach to training relaxation labeling processes

In a recent work, a learning procedure for relaxation labeling algorithms has been introduced which involves minimizing a certain cost function with classical gradient methods. Although the results obtained so far are extremely promising, the gradient-based learning algorithm suffers from some inher...

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Bibliographic Details
Published in:Pattern recognition letters 1995, Vol.16 (10), p.1069-1078
Main Authors: Pelillo, Marcello, Abbattista, Fabio, Maffione, Angelo
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Genetic algorithms
Learning
Line labeling
Part-of-speech disambiguation
Relaxation labeling
Speech and sound recognition and synthesis. Linguistics
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Summary:In a recent work, a learning procedure for relaxation labeling algorithms has been introduced which involves minimizing a certain cost function with classical gradient methods. Although the results obtained so far are extremely promising, the gradient-based learning algorithm suffers from some inherent drawbacks that could prevent its application to real-world problems of practical interest. Essentially, these include the inability to escape from local minima and its computational complexity. In this paper, we propose using genetic algorithms to solve the relaxation labeling learning problem in an attempt to overcome the difficulties with the gradient algorithm. Experiments are presented which demonstrate the superiority of the proposed approach both in terms of quality of solutions and robustness.
ISSN:0167-8655
1872-7344
DOI:10.1016/0167-8655(95)00061-K