Using K-Winner Machines for domain analysis
The K-Winner Machine (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 effectiveness. The aspects involved by the analytical properties of the KWM first drive a theoretical analysis of the rejection performance. Then the paper shows that the KWM classification process can also be profitably used for domain inspection. Novel theorems connect the outputs of KWMs directly to the class-separating boundaries in the data space. Empirical evidence eventually supports the intuitive result that smaller confidence values characterize boundary regions.
k-winner machine domain analysis mixed supervised-unsupervised learning vector quantization structural risk minimization