Generalization Bounds on Multi-Kernel Learning with Mixed Datasets

05/15/2022

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by   Lan V. Truong, et al.

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This paper presents novel generalization bounds for the multi-kernel learning problem. Motivated by applications in sensor networks, 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(√(log m)) dependency on the number of base kernels and O(1/√(n)) dependency on the number of training samples. However, some O(1/√(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.