Abstract
Classification based on the Bayes criterion minimizes the probability of classification error. In order to apply this criterion, one has to know the probability densities of each class of data. Based on the Parzen density estimation, a new method is derived. The number of operations involved in the Parzen estimation is reduced by using the vector quantization property of Self Organizing Feature Mapping (SOFM). The width of the kernels is made dependent on the variance of the samples in the clusters.
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© 1995 Springer-Verlag London Limited
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Lokerse, S.H., Veelenturf, L.P.J., Beltman, J.G. (1995). Density Estimation Using SOFM and Adaptive Kernels. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_39
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_39
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
Online ISBN: 978-1-4471-3087-1
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