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1999 (v1)Publication
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2004 (v1)Publication
The K-WinnerMachine (KWM) model combines unsupervised with supervised training paradigms, and builds up a family of nested classifiers that differ in their expected generalization performances. A KWM allows members of the classifier family to reject a test pattern, and predicting the rejection rate is a crucial issue to the ultimate method...
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2006 (v1)Publication
While addressing Vector Quantization (VQ) as a general paradigm for data representation, the paper adopts the K-winner Machine model as a case study, which provides a reference for analyzing both theoretical and implementation aspects. The design of vector quantizers often requires that the (often overlooked) dichotomy between 'analogue'...
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2004 (v1)Publication
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1990 (v1)Publication
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1999 (v1)Publication
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