Abstract:
This contribution studies the problem of learning sparse, nonparametric models from observations drawn from an arbitrary, unknown distribution. This specific problem lead...Show MoreMetadata
Abstract:
This contribution studies the problem of learning sparse, nonparametric models from observations drawn from an arbitrary, unknown distribution. This specific problem leads us to an algorithm extending techniques for Multiple Kernel Learning (MKL), functional ANOVA models and the Component Selection and Smoothing Operator (COSSO). The key element is to use a data-dependent regularization scheme adapting to the specific distribution underlying the data. We then present empirical evidence supporting the proposed learning algorithm.
Date of Conference: 14-16 June 2010
Date Added to IEEE Xplore: 14 October 2010
ISBN Information: