Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Bayesian Passive-Aggressive Learning   Online Passive-Aggressive (PA) learning is an effective framework for
performing max-margin online learning. But the deterministic formulation and
estimated single large-margin model could limit its capability in discovering
descriptive structures underlying complex data. This pa- per presents online
Bayesian Passive-Aggressive (BayesPA) learning, which subsumes the online PA
and extends naturally to incorporate latent variables and perform nonparametric
Bayesian inference, thus providing great flexibility for explorative analysis.
We apply BayesPA to topic modeling and derive efficient online learning
algorithms for max-margin topic models. We further develop nonparametric
methods to resolve the number of topics. Experimental results on real datasets
show that our approaches significantly improve time efficiency while
maintaining comparable results with the batch counterparts.

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The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org