K-winner machines for pattern classification

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 lo...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2001-03, Vol.12 (2), p.371-385
Main Authors: Ridella, S., Rovetta, S., Zunino, R.
Format: Article
Language:English
Subjects:
Calibration
Classification
Classifiers
Confidence
Consistency
Mathematical models
Neural networks
Optical wavelength conversion
Pattern classification
Prototypes
Risk management
Space technology
Support vector machine classification
Support vector machines
Testing
Vector quantization
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Summary: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.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/72.914531