Name
Abstract | ||
|---|---|---|
A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining discriminantly informative features, and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for nonparametric classification is proposed. Experimental results show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/21.229456 | IEEE Transactions on Systems, Man and Cybernetics |
Keywords | Field | DocType |
decision boundary,pattern recognition,estimation theory,feature extraction,nonparametric classification,normal vectors,decision theory,parzen density estimation,boundaries,extraction,algorithms,estimating,density estimation | Density estimation,Nonparametric classification,Pattern recognition,Computer science,Nonparametric statistics,Feature extraction,Decision theory,Artificial intelligence,Estimation theory,Classifier (linguistics),Decision boundary,Machine learning | Journal |
Volume | Issue | ISSN |
23 | 2 | 0018-9472 |
Citations | PageRank | References |
17 | 4.85 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chulhee Lee | 1 | 454 | 86.37 |
| David A. Landgrebe | 2 | 807 | 125.38 |