Name
Abstract | ||
|---|---|---|
Kernel methods have been successfully used in many practical machine learning problems. However, the problem of choosing a suitable kernel is left to practitioners. One method to select the optimal kernel is to learn a linear combination of element kernels. A framework of multiple kernel learning based on conditional entropy minimization criterion (MCEM) has been proposed and it has been shown to work well for, e.g., speaker recognition tasks. In this paper, a computationally efficient implementation for MCEM, which utilizes sequential quadratic programming, is formulated. Through a comparative experiment to conventional MCEM algorithm on a speaker verification task, the proposed method is shown to offer comparable verification accuracy with considerable improvement in computational speed. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | entropy-based multiple kernel learning,quadratic programming,conditional entropy minimization criterion,element kernel linear combination learning,learning (artificial intelligence),sequential quadratic programming,speaker recognition,computational speed improvement,mcem algorithm,speaker recognition tasks,optimal kernel selection,machine learning problems,entropy,speaker verification task,learning artificial intelligence |
Field | DocType | ISSN |
Least squares support vector machine,Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Computer science,Multiple kernel learning,Tree kernel,Speaker recognition,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideitsu Hino | 1 | 99 | 25.73 |
| Tetsuji Ogawa | 2 | 73 | 27.96 |