Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Faster method for Deep Belief Network based Object classification using
  DWT   A Deep Belief Network (DBN) requires large, multiple hidden layers with high
number of hidden units to learn good features from the raw pixels of large
images. This implies more training time as well as computational complexity. By
integrating DBN with Discrete Wavelet Transform (DWT), both training time and
computational complexity can be reduced. The low resolution images obtained
after application of DWT are used to train multiple DBNs. The results obtained
from these DBNs are combined using a weighted voting algorithm. The performance
of this method is found to be competent and faster in comparison with that of
traditional DBNs.

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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: physics