Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GAdaBoost: Accelerating Adaboost Feature Selection with Genetic
  Algorithms   Boosted cascade of simple features, by Viola and Jones, is one of the most
famous object detection frameworks. However, it suffers from a lengthy training
process. This is due to the vast features space and the exhaustive search
nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to
accelerate the training procedure through natural feature selection.
Specifically, we propose to limit Adaboost search within a subset of the huge
feature space, while evolving this subset following a Genetic Algorithm.
Experiments demonstrate that our proposed GAdaboost is up to 3.7 times faster
than Adaboost. We also demonstrate that the price of this speedup is a mere
decrease (3%, 4%) in detection accuracy when tested on FDDB benchmark face
detection set, and Caltech Web Faces respectively.

10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess