Generalization Bounds on Multi-Kernel Learning with Mixed Datasets

Autor:	Truong, Lan V.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Update Marton Coupling. Under review for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.07313 Zobrazit plný text záznamu View this record from Arxiv