A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems
Published in arXiv (Cornell University) • Jan 21, 2014
NobleIDNI4P72W74R11S09
Authors:,,
Cong Li
Michael Georgiopoulos
Georgios C. Anagnostopoulos
Abstract
Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including Multi-Task Learni...
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