@article {31,
	title = {A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems},
	journal = {Neural Networks and Learning Systems, IEEE Transactions on},
	volume = {25},
	year = {2013},
	month = {July},
	pages = {1287-1297},
	abstract = {<p>Over the past few years, multiple 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 multitask learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a nontrivial accomplishment. In this paper we present a general multitask multiple kernel learning (MT-MKL) framework that subsumes well-known MT-MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely partially-shared common space MT-MKL, and demonstrate its merits through experimentation.</p>
},
	keywords = {Algorithm design and analysis, Closed-form solutions, Kernel, Learning systems, Machine learning, Optimization, optimization methods, pattern recognition, supervised learning, Support vector machines, support vector machines (SVMs), support vector machines (SVMs)., Vectors},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2013.2291772},
	author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos}
}
