Multitask Classification Hypothesis Space With Improved Generalization Bounds

Keywords

Machine learning; pattern recognition; statistical learning; supervised learning; support vector machines

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 bounds and validate this paper's analysis.

Publication Date

7-1-2015

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

Volume

26

Issue

7

Number of Pages

1468-1479

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TNNLS.2014.2347054

Socpus ID

85027957918 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85027957918

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