An approach to the evaluation of the performance of a discrete classifier

Pattern Recognition Letters

Volumn 23, Issue 1-3, 2002, Pages 227-233

  Berikov, Vladimir B ^a  

^a SOBOLEV INSTITUTE OF MATHEMATICS   (Russian Federation)

Author keywords

Classifier analysis; Error rate estimation; Naive Bayes

Indexed keywords

CLASSIFIERS; PROBABILITY; RANDOM PROCESSES; STATISTICS;

DISCRETE CLASSIFIERS;

PATTERN RECOGNITION;

IMAGE CLASSIFICATION; PATTERN RECOGNITION; STATISTICAL ANALYSIS;

EID: 0036132373     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(01)00119-2     Document Type: Article

References (17)

1. Regression trees for analysis of mutational spectra in nucleotide sequences
2. Independence of measurements and the mean recognition accuracy
3. The mean recognition performance for independent distributions
4. The subclass approach for mutational spectrum analysis: Application of the SEM algorithm
5. On generalizations of Tchebychef's inequality
6. Ob ozhidaemoy oshibke klassifikatora dlya kachestvennykh priznakov
7. On the mean accuracy of statistical pattern recognizers
8. Dimensionality and sample size considerations in pattern recognition practice. Classification, pattern recognition and reduction of dimensionality
9. Statistical pattern recognition: A review
10. Construction of recognition decision rules in the class of logical functions
11. On dimensionality, sample size, and classification error of nonparametric linear classification algorithms
12. Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
13. Small sample size effects in statistical pattern recognition: Recommendations for practitioners
14. The average loss in classification problems with limited sample size