@article {48,
	title = {Multitask Classification Hypothesis Space With Improved Generalization Bounds},
	journal = {Neural Networks and Learning Systems, IEEE Transactions on},
	volume = {26},
	year = {2015},
	pages = {1468-1479},
	abstract = {This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading  to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived  and validate this paper{\textquoteright}s analysis.},
	keywords = {Context, Hilbert space, Kernel, Learning systems, Machine learning, pattern recognition, statistical learning, supervised learning, Support vector machines, Training, Upper bound},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2014.2347054},
	author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos}
}
@article {30,
	title = {Pareto-Path Multitask Multiple Kernel Learning},
	journal = {Neural Networks and Learning Systems, IEEE Transactions on},
	volume = {26},
	year = {2015},
	pages = {51-61},
	chapter = {51},
	abstract = {<p>A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.</p>
},
	keywords = {Machine learning, optimization methods, pattern recognition, supervised learning, support vector machines (SVM).},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2014.2309939},
	author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos}
}
@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}
}
