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Bayesian Nonparametric Kernel-Learning | Junier Oliva
; Avinava Dubey
; Barnabas Poczos
; Jeff Schneider
; Eric P. Xing
; | Date: |
29 Jun 2015 | Abstract: | Kernel methods are ubiquitous tools in machine learning. They have proven to
be effective in many domains and tasks. Yet, kernel methods often require the
user to select a predefined kernel to build an estimator with. However, there
is often little reason for the a priori selection of a kernel. Even if a
universal approximating kernel is selected, the quality of the finite sample
estimator may be greatly effected by the choice of kernel. Furthermore, when
directly applying kernel methods, one typically needs to compute a $N imes N$
Gram matrix of pairwise kernel evaluations to work with a dataset of $N$
instances. The computation of this Gram matrix precludes the direct application
of kernel methods on large datasets. In this paper we introduce Bayesian
nonparmetric kernel (BaNK) learning, a generic, data-driven framework for
scalable learning of kernels. We show that this framework can be used for
performing both regression and classification tasks and scale to large
datasets. Furthermore, we show that BaNK outperforms several other scalable
approaches for kernel learning on a variety of real world datasets. | Source: | arXiv, 1506.8776 | Services: | Forum | Review | PDF | Favorites |
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