title: K-winner machines for pattern classification.
abstract: The paper describes the K-winner machine (KWM) model for classification. KWM training uses unsupervised vector quantization and subsequent calibration to label data-space partitions. A K-winner classifier seeks the largest set of best-matching prototypes agreeing on a test pattern, and provides a local-level measure of confidence. A theoretical analysis characterizes the growth function of a K-winner classifier, and the result leads to tight bounds to generalization performance. The method proves suitable for high-dimensional multiclass problems with large amounts of data. Experimental results on both a synthetic and a real domain (NIST handwritten numerals) confirm the approach effectiveness and the consistency of the theoretical framework.
authors.affiliation: DIBE, Department Biophysical and Electronic Engineering, University of Genoa, 16145 Genova, Italy. ridella@dibe.unige.it
authors.forename: S
authors.initials: S
authors.lastname: Ridella
authors.forename: S
authors.initials: S
authors.lastname: Rovetta
authors.forename: R
authors.initials: R
authors.lastname: Zunino