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arXiv · 2205.07313

Generalization Bounds on Multi-Kernel Learning with Mixed Datasets

Lan V. Truong
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Abstract

This paper presents novel generalization bounds for the multi-kernel learning problem. Motivated by applications in sensor networks and spatial-temporal models, we assume that the dataset is mixed where each sample is taken from a finite pool of Markov chains. Our bounds for learning kernels admit $O(\sqrt{\log m})$ dependency on the number of base kernels and $O(1/\sqrt{n})$ dependency on the number of training samples. However, some $O(1/\sqrt{n})$ terms are added to compensate for the dependency among samples compared with existing generalization bounds for multi-kernel learning with i.i.d. datasets.

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Lan V. Truong. 2022-05-15. Generalization Bounds on Multi-Kernel Learning with Mixed Datasets. https://arxiv.org/abs/2205.07313

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