Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Infinite Mixtures of Multivariate Gaussian Processes   This paper presents a new model called infinite mixtures of multivariate
Gaussian processes, which can be used to learn vector-valued functions and
applied to multitask learning. As an extension of the single multivariate
Gaussian process, the mixture model has the advantages of modeling multimodal
data and alleviating the computationally cubic complexity of the multivariate
Gaussian process. A Dirichlet process prior is adopted to allow the (possibly
infinite) number of mixture components to be automatically inferred from
training data, and Markov chain Monte Carlo sampling techniques are used for
parameter and latent variable inference. Preliminary experimental results on
multivariate regression show the feasibility of the proposed model.

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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
2
The categories of this paper are: math
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The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
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The categories of this paper are: eess
5
The categories of this paper are: gr-qc
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The categories of this paper are: funct-an