IEEE Trans Neural Netw Learn Syst - Multitask Classification Hypothesis Space With Improved Generalization Bounds.

Tópicos

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Resumo

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 bounds and validate this paper's analysis.

Resumo Limpo

paper present pair hypothesi space hss vectorvalu function intend use context multitask classif parameter element reproduc kernel hilbert space impos featur map common task one assum map fix general one learn map via multipl kernel learn new hss empir rademach complexitybas general bound deriv shown tighter bound particular hs appear recent literatur lead improv perform matter fact latter hs shown special case base equival grouplasso type hss propos hss util toward correspond support vector machinebas formul final experiment result multitask learn problem underlin qualiti deriv bound valid paper analysi

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