Learning and Decision-Making in the Framework of Fuzzy Lattices (2000)
| Venue: | in New Learning Techniques in Computational Intelligence Paradigms |
| Citations: | 10 - 3 self |
BibTeX
@INPROCEEDINGS{Kaburlasos00learningand,
author = {Vassilis G. Kaburlasos and Vassilios Petridis},
title = {Learning and Decision-Making in the Framework of Fuzzy Lattices},
booktitle = {in New Learning Techniques in Computational Intelligence Paradigms},
year = {2000},
pages = {55--96},
publisher = {CRC Press}
}
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Abstract
A novel theoretical framework is delineated for supervised and unsupervised learning. It is called framework of fuzzy lattices, or FLframework for short, and it suggests mathematically sound tools for dealing separately and/or jointly with disparate types of data including vectors of numbers, fuzzy sets, symbols, etc. Specific schemes are proposed for clustering and classification having the capacity to deal with both missing and don't care data values; the schemes in question can be implemented as neural networks. The proposed learning schemes are employed here for pattern recognition on seven data sets including benchmark data sets, and the results are compared with those ones by various learning techniques from the literature. Finally, aiming at a mutual cross-fertilization, the FL-framework is associated with established theories for learning and/or decision-making including probability theory, fuzzy set theory, Bayesian decision-making, theory of evidence, and adaptive resonance t...
Keyphrases
fuzzy lattice care data value adaptive resonance novel theoretical framework pattern recognition fuzzy set unsupervised learning fuzzy set theory specific scheme bayesian decision-making benchmark data set sound tool data set mutual cross-fertilization learning scheme probability theory disparate type neural network
