Generalization Bounds on Multi-Kernel Learning with Mixed Datasets
This work addresses generalization guarantees for multi-kernel learning in applications like sensor networks and spatial-temporal models, but it is incremental as it extends existing bounds to non-i.i.d. data.
The paper tackles the problem of deriving generalization bounds for multi-kernel learning with mixed datasets from Markov chains, achieving bounds with O(√log m) dependency on the number of kernels and O(1/√n) on sample size, with additional O(1/√n) terms to account for sample dependencies.
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.