Generation of Polynomial Discriminant Functions for Pattern Recognition
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Summary
The method is based on nonparametric estimation of a probability density function for each category to be classified so that the Bayes decision rule can be used for classification and has good extrapolating ability even when the number of training patterns is quite small.
- Type
- article
- Published
- 1967-06-01
- Cited by
- 260
- References
- 7
- OpenAlex
- https://openalex.org/W2161596194
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:38691360
Keywords
Nonparametric statistics, Polynomial, Discriminant, Pattern recognition (psychology), Decision rule
References
- Vectorcardiographic diagnosis using the polynomial discriminant method of pattern recognition.
- Estimation of Probability Density
- Learning Matrices and Their Applications
- Pattern recognition by an adaptive process of sample set construction
- On Estimation of a Probability Density Function and Mode
- Nearest neighbor pattern classification
- The use of an adaptive threshold element to design a linear optimal pattern classifier
- Introduction to the Theory of Statistics.
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- Applications of the Pattern Recognition Techniques at Electricite de France
- SYSTEM DIAGNOSIS USING A POLYNOMIAL DISCRIMINANT FUNCTION
- Computer simulation of pattern recognition using statistical decision functions
- THEORIES AND METHODS IN CLASSIFICATION: A REVIEW
- On the accuracy of statistical pattern recognizers
- Visual Event Detection
- Active Control of Complex Systems via Dynamic (Recurrent) Neural Networks
- Geochemical and geostatistical evaluation of American Flats-Silverton planning units, San Juan volcanic province, Colorado
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- Dimension Reduction Techniques for Training Polynomial Networks
- Using polynomial networks for speech recognition
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