Continuous Kernel Learning

Conference paper

DOI: 10.1007/978-3-319-46227-1_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)
Cite this paper as:
Moeller J., Srikumar V., Swaminathan S., Venkatasubramanian S., Webb D. (2016) Continuous Kernel Learning. In: Frasconi P., Landwehr N., Manco G., Vreeken J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Lecture Notes in Computer Science, vol 9852. Springer, Cham

Abstract

Kernel learning is the problem of determining the best kernel (either from a dictionary of fixed kernels, or from a smooth space of kernel representations) for a given task. In this paper, we describe a new approach to kernel learning that establishes connections between the Fourier-analytic representation of kernels arising out of Bochner’s theorem and a specific kind of feed-forward network using cosine activations. We analyze the complexity of this space of hypotheses and demonstrate empirically that our approach provides scalable kernel learning superior in quality to prior approaches.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA

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