Name
Abstract | ||
|---|---|---|
•CGMKL realizes the multi-class classification under the MEKL framework through combining the softmax function and MEKL. By doing so, the MEKL enriches the expressions of sample and greatly improves the classification ability of the softmax function.•CGMKL offers the complementary information between different kernel spaces by introducing a regularization term RU, which keeps consistency outputs of samples in different kernel spaces. By doing so, classifiers in different kernel spaces can learn from each other and keep collaborative working.•CGMKL makes the output trend of data suit for classification through introducing a regularization term RG, which reduces the within-class distance of the outputs of samples. By doing so, the classification result exhibits a geometric feature. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2019.107050 | Pattern Recognition |
Keywords | Field | DocType |
Multi-class classification,Empirical kernel mapping,Multiple empirical kernel learning,Regularized learning | Kernel (linear algebra),Data set,Collaborative learning,Softmax function,Pattern recognition,Regularization (mathematics),Artificial intelligence,Multi kernel,Machine learning,Mathematics,Multiclass classification | Journal |
Volume | Issue | ISSN |
99 | 1 | 0031-3203 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Wang | 1 | 50 | 20.04 |
| Zonghai Zhu | 2 | 11 | 3.54 |
| Dongdong Li | 3 | 15 | 8.34 |