Name
Abstract | ||
|---|---|---|
The minimum-distance classifier learns a single mean prototype for each class and uses a nearest neighbor approach for classification. A problem arises when classes cannot be accurately represented using a single prototype; multiple prototypes may be necessary. Our approach is to find groups of examples for each of the classes, generalize these groups into prototypes using a mean representation, and then classify using a nearest neighbor approach. |
Year | Venue | Keywords |
|---|---|---|
1997 | AAAI/IAAI | classification problem,k means clustering,nearest neighbor |
Field | DocType | ISBN |
k-nearest neighbors algorithm,Fuzzy clustering,Pattern recognition,Correlation clustering,Computer science,Nearest-neighbor chain algorithm,Artificial intelligence,Classifier (linguistics),Cluster analysis,Machine learning,Nearest neighbor search | Conference | 0-262-51095-2 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piew Datta | 1 | 105 | 24.65 |