Abstract
This chapter introduces the basic concepts and notation of unsupervised learning neural networks. Unsupervised networks are useful for analyzing data without having the desired outputs; in this case, the neural networks evolve to capture density characteristics of a data phase. We will describe in some detail competitive learning networks, Kohonen self-organizing networks, learning vector quantization, and Hopfield networks. We will also show some examples of these networks to illustrate their possible application in solving real-world problems in pattern recognition.
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Melin, P., Castillo, O. Unsupervised Learning Neural Networks. In: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing. Studies in Fuzziness and Soft Computing, vol 172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32378-5_5
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DOI: https://doi.org/10.1007/978-3-540-32378-5_5
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24121-8
Online ISBN: 978-3-540-32378-5
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