Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bayesian Approach for Online Classifier Ensemble   We propose a Bayesian approach for recursively estimating the classifier
weights in online learning of a classifier ensemble. In contrast with past
methods, such as stochastic gradient descent or online boosting, our approach
estimates the weights by recursively updating its posterior distribution. For a
specified class of loss functions, we show that it is possible to formulate a
suitably defined likelihood function and hence use the posterior distribution
as an approximation to the global empirical loss minimizer. If the stream of
training data is sampled from a stationary process, we can also show that our
approach admits a superior rate of convergence to the expected loss minimizer
than is possible with standard stochastic gradient descent. In experiments with
real-world datasets, our formulation often performs better than
state-of-the-art stochastic gradient descent and online boosting algorithms.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol