Name
Abstract | ||
|---|---|---|
A k-nearest-neighbor classifier is approximated by a labeled cell classifier that recursively labels the nodes of a hierarchically organized reference sample (e.g., a k-d tree) if a local estimate of the conditional Bayes risk is sufficiently small. Simulations suggest that the labeled cell classifier is significantly faster than k-d tree implementations for problems with small Bayes risk, and may be more accurate as a larger reference sample can be examined in a fixed amount of time |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/ICPR.1998.711276 | Pattern Recognition, 1998. Proceedings. Fourteenth International Conference |
Keywords | Field | DocType |
Bayes methods,approximation theory,pattern classification,trees (mathematics),Bayes risk,fast approximation,feature space,labelled cell classifier,nearest-neighbor classifier,pattern classification,trees | k-nearest neighbors algorithm,Pattern recognition,Naive Bayes classifier,Computer science,Artificial intelligence,Margin classifier,Classifier (linguistics),Bayes error rate,Binary search tree,Quadratic classifier,Bayes' theorem | Conference |
Volume | ISSN | ISBN |
1 | 1051-4651 | 0-8186-8512-3 |
Citations | PageRank | References |
4 | 0.44 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro M. Palau | 1 | 4 | 0.44 |
| Robert R. Snapp | 2 | 56 | 52.96 |