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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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