Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Super-Samples from Kernel Herding   We extend the herding algorithm to continuous spaces by using the kernel
trick. The resulting "kernel herding" algorithm is an infinite memory
deterministic process that learns to approximate a PDF with a collection of
samples. We show that kernel herding decreases the error of expectations of
functions in the Hilbert space at a rate O(1/T) which is much faster than the
usual O(1/pT) for iid random samples. We illustrate kernel herding by
approximating Bayesian predictive distributions.

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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
2
The categories of this paper are: math