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- W205256325 abstract "Efficient feature extraction is an important operation for any signal processing or communication system. The learning algorithms of the extraction should be low in quantization distortion, efficient for coding, sufficient for discrimination, accurate for data structure, and follow the sample distribution; minimum error should not be the only criterion. There are four major unsupervised competitive learning algorithms with different criteria for optimization and iterative learning. These algorithms are K-mean clustering, hierarchical clustering, Kohonen self-organizing feature maps, and added conscience competitive learning. With these algorithms, it has been found that the probability density of the representation set is important in the relationship between neighboring clusters. Two representations that are close in distance should also be close in the node connection space. This is true if the signals are time-sequential patterns. The present study implements an unsupervised learning algorithm that can combine existing algorithms to accomplish different criteria. This algorithm is called emultiple criteria competitive learning." @default.
- W205256325 created "2016-06-24" @default.
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- W205256325 date "1991-01-01" @default.
- W205256325 modified "2023-09-23" @default.
- W205256325 title "A LEARNING ALGORITHM WITH MULTIPLE CRITERIA FOR SELF-ORGANIZING FEATURE MAPS" @default.
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- W205256325 doi "https://doi.org/10.1016/b978-0-444-89178-5.50087-7" @default.
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