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• W2163535761 abstract "Supervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These hybrid approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as rule-based. They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the Waveform Recognition Problem proposed by Breiman et al., 4 which is difficult for rule based approaches." @default.
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• W2163535761 date "1998-08-01" @default.
• W2163535761 modified "2023-09-29" @default.
• W2163535761 title "Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods" @default.
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• W2163535761 doi "https://doi.org/10.1142/s0218001498000336" @default.
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