Abstract
Classifiers derived by learning vector quantization (LVQ) have well-defined decision regions that can be combined to construct a more accurate classifier. A given point may be included in a number of decision regions associated with different LVQ classifiers. The relative densities of classes in each region can be combined to obtain a final classification. The method allows useful inferences from small training sets, which is needed for problems involving large variations within each class. In an application of this method to the recognition of handwritten digits, it is shown that the classifier can be improved almost monotonically without suffering from over-adaptation to the training data.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Learning vector quantizationArtificial intelligenceClassifier (UML)Computer sciencePattern recognition (psychology)Vector quantizationArtificial neural networkMachine learningSupport vector machine
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 818-821
- Citations
- 21
- Access
- Closed
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Cite This
T.K. Ho
(2002).
Recognition of handwritten digits by combining independent learning vector quantizations.
, 818-821.
https://doi.org/10.1109/icdar.1993.395612
Identifiers
- DOI
- 10.1109/icdar.1993.395612