Learning quantitative features in a symbolic environment
Pattern classification is considered a major task both in Artificial Intelligence and in Pattern Recognition. Artificial Intelligence developed symbolic methods for learning classification rules which are are effective in order to acquire the structure of the knowledge, whereas Pattern Recognition developed methods effective for learning numerical features such as weights and thresholds. However, both structural and numeric knowledge is involved in classifying complex patterns. This paper presents a method for dealing with numeric features in a learning framework based on a first order logic. The method has been implemented and works in two steps. In the first step, tentative numeric features are learned together with symbolic features, whereas in the second one the numeric knowledge is refined using a standard genetic algorithm.
The implementation is evaluated on a problem of pattern recognition, already described in the literature by the authors. The results are encouraging and show the viability of the approach.
KeywordsLearning from Examples Inductive Learning Genetic Algorithms
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