Jain, A. ; Vishwanathan, S.V.N. ; Varma, M. (2012) SPG-GMKL: Generalized Multiple Kernel Learning with a Million Kernels In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 12–16, 2012, Beijing, China.
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Abstract
Multiple Kernel Learning (MKL) aims to learn the kernel in an SVM from training data. Many MKL formulations have been proposed and some have proved effective in certain applications. Nevertheless, as MKL is a nascent field, many more formulations need to be developed to generalize across domains and meet the challenges of real world applications. However, each MKL formulation typically necessitates the development of a specialized optimization algorithm. The lack of an efficient, general purpose optimizer capable of handling a wide range of formulations presents a significant challenge to those looking to take MKL out of the lab and into the real world.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to Knowledge Discovery and Data Mining. |
| ID Code: | 119692 |
| Deposited On: | 16 Jun 2021 08:52 |
| Last Modified: | 16 Jun 2021 08:52 |
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