On the dominance of non-parametric Bayes rule discriminant algorithms in high dimensions

Pattern Recognition

Volumn 12, Issue 6, 1980, Pages 355-368

  Van Ness, John ^a  

^a NONE

Author keywords

Dimension reduction; Discriminant analysis; Non parametric methods; Pattern recognition; Variable selection

Indexed keywords

PATTERN RECOGNITION SYSTEMS;

PROBABILITY;

BAYES THEOREM; COMPUTER ANALYSIS; DIAGNOSIS; DIGITAL COMPUTER; DISCRIMINANT ANALYSIS; PATTERN RECOGNITION;

EID: 0019226255     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/0031-3203(80)90012-6     Document Type: Article

References (15)

1. The problem of too many measurements in pattern recognition and prediction
2. Admissible clustering procedures
3. Considerations of sample and feature size
4. A Comparison of some multivariate discriminant procedures
5. Comparison of some density estimate classification procedures
6. Discriminant functions when covariance matrices are unequal
7. A simulative comparison of linear, quadratic and kernal discrimination
8. On the effects of dimension in discriminant analysis
9. Bootstrap methods another look at the jackknife