Name
Abstract | ||
|---|---|---|
In this article we investigate the field of Hilbertian metrics on probability measures. Since they are very versatile and can therefore be applied in various problems they are of great interest in kernel methods. Quit recently Topsoe and Fuglede introduced a family of Hilbertian metrics on probability measures. We give basic properties of the Hilbertian metrics of this family and other used metrics in the literature. Then we propose an extension of the considered metrics which incorporates structural information of the probability space into the Hilbertian metric. Finally we compare all proposed metrics in an image and text classification problem using histogram data. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-28649-3_33 | PATTERN RECOGNITION |
Keywords | Field | DocType |
artificial intelligence,pattern recognition,histogram,image classification,probability measure,information retrieval,kernel method,metric space,metric,content analysis | Equivalence of metrics,Histogram,Color space,Computer science,Probability measure,Support vector machine,Artificial intelligence,Metric space,Kernel method,Contextual image classification,Machine learning | Conference |
Volume | ISSN | Citations |
3175 | 0302-9743 | 6 |
PageRank | References | Authors |
2.11 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Hein | 1 | 663 | 62.80 |
| Thomas Navin Lal | 2 | 1982 | 122.60 |
| Olivier Bousquet | 3 | 4593 | 359.65 |