Multi-task Learning in vector-valued reproducing kernel Banach spaces with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1008" altimg="si1021.svg"><mml:msup><mml:mrow><mml:mi>?</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math> norm
نویسندگان
چکیده
Targeting at sparse multi-task learning, we consider regularization models with an ?1 penalty on the coefficients of kernel functions. In order to provide a method for this model, construct class vector-valued reproducing Banach spaces norm. The notion admissible kernels is proposed so that constructed could have desirable properties including crucial linear representer theorem. Such are related bounded Lebesgue constants interpolation question. We study constant and examples kernels. Furthermore, present numerical experiments both synthetic data real-world benchmark demonstrate advantages construction models.
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ژورنال
عنوان ژورنال: Journal of Complexity
سال: 2021
ISSN: ['1090-2708', '0885-064X']
DOI: https://doi.org/10.1016/j.jco.2020.101514