Maximum Likelihood Topographic Map Formation
نویسنده
چکیده
We introduce a new unsupervised learning algorithm for kernel-based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error (vector quantization), log-likelihood, and Kullback-Leibler divergence.
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ورودعنوان ژورنال:
- Neural computation
دوره 17 3 شماره
صفحات -
تاریخ انتشار 2005