Name
Abstract | ||
|---|---|---|
Showing the nearest neighbor is a useful explanation for the result of an automatic classification. Given, expert defined, distance measures may be improved on the basis of a training set. We study several proposals to optimize such measures for nearest neighbor classification, explicitly including non-Euclidean measures. Some of them may directly improve the distance measure, others may construct a dissimilarity space for which the Euclidean distances show significantly better performances. Results are application dependent and raise the question what characteristics of the original distance measures influence the possibilities of metric learning. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-44415-3_19 | Lecture Notes in Computer Science |
Field | DocType | Volume |
k-nearest neighbors algorithm,Training set,Data mining,Pattern recognition,Artificial intelligence,Nearest-neighbor chain algorithm,Euclidean geometry,Large margin nearest neighbor,Mathematics,Nearest neighbor search,Distance measures | Conference | 8621 |
ISSN | Citations | PageRank |
0302-9743 | 7 | 0.47 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert P. W. Duin | 1 | 4322 | 336.00 |
| Manuele Bicego | 2 | 1028 | 72.30 |
| Mauricio Orozco-Alzate | 3 | 79 | 17.27 |
| Sang-Woon Kim | 4 | 310 | 28.20 |
| Marco Loog | 5 | 1796 | 154.31 |