Name
Title | ||
|---|---|---|
Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic. |
Abstract | ||
|---|---|---|
Entropy-based divergence measures have proven their effectiveness in many areas of computer vision and pattern recognition. However, the complexity of their implementation might be prohibitive in resource-limited applications, as they require estimates of probability densities which are expensive to compute directly for high-dimensional data. In this paper, we investigate the usage of a non-parame... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2862352 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Probability density function,Training data,Feature extraction,Pattern recognition,Nearest neighbor methods | Training set,Divergence,Pattern recognition,Test statistic,Measurement uncertainty,Nonparametric statistics,Feature extraction,Artificial intelligence,Probability density function,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 12 | 1057-7149 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sally Ghanem | 1 | 0 | 0.68 |
| Hamid Krim | 2 | 520 | 59.69 |
| Hamilton Scott Clouse | 3 | 1 | 0.77 |
| Wesam Sakla | 4 | 14 | 1.95 |