Name
Abstract | ||
|---|---|---|
We present a new algorithm for multi-class classification with multiple kernels. Our algorithm is based on a natural notion of the multi-class margin of a kernel. We show that larger values of this quantity guarantee the existence of an accurate multi-class predictor and also define a family of multiple kernel algorithms based on the maximization of the multi-class margin of a kernel (M^3K). We present an extensive theoretical analysis in support of our algorithm, including novel multi-class Rademacher complexity margin bounds. Finally, we also report the results of a series of experiments with several data sets, including comparisons where we improve upon the performance of state-of-the-art algorithms both in binary and multi-class classification with multiple kernels. |
Year | Venue | Field |
|---|---|---|
2013 | ICML | Kernel (linear algebra),Margin (machine learning),Pattern recognition,Radial basis function kernel,Computer science,Rademacher complexity,Tree kernel,Artificial intelligence,Variable kernel density estimation,Machine learning,Maximization,Multiclass classification |
DocType | Citations | PageRank |
Conference | 11 | 0.55 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Corinna Cortes | 1 | 6574 | 1120.50 |
| Mehryar Mohri | 2 | 4502 | 448.21 |
| Afshin Rostamizadeh | 3 | 911 | 44.15 |