Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass
  Plug-in Classifiers   We prove new fast learning rates for the one-vs-all multiclass plug-in
classifiers trained either from exponentially strongly mixing data or from data
generated by a converging drifting distribution. These are two typical
scenarios where training data are not iid. The learning rates are obtained
under a multiclass version of Tsybakov's margin assumption, a type of low-noise
assumption, and do not depend on the number of classes. Our results are general
and include a previous result for binary-class plug-in classifiers with iid
data as a special case. In contrast to previous works for least squares SVMs
under the binary-class setting, our results retain the optimal learning rate in
the iid case.

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The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess