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arXiv · 1312.2606

Multi-Task Classification Hypothesis Space with Improved Generalization Bounds

Cong Li·Michael Georgiopoulos·Georgios C. Anagnostopoulos
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Abstract

This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification. It extends similar hypothesis spaces that have previously considered in the literature. Assuming this space, an improved Empirical Rademacher Complexity-based generalization bound is derived. The analysis is itself extended to an MKL setting. The connection between the proposed hypothesis space and a Group-Lasso type regularizer is discussed. Finally, experimental results, with some SVM-based Multi-Task Learning problems, underline the quality of the derived bounds and validate the paper's analysis.

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Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos. 2013-12-09. Multi-Task Classification Hypothesis Space with Improved Generalization Bounds. https://arxiv.org/abs/1312.2606

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