Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Convolutional Neural Network Ensembles using ECOC   Deep neural networks have enhanced the performance of decision making systems
in many applications including image understanding, and further gains can be
achieved by constructing ensembles. However, designing an ensemble of deep
networks is often not very beneficial since the time needed to train the
networks is very high or the performance gain obtained is not very significant.
In this paper, we analyse error correcting output coding (ECOC) framework to be
used as an ensemble technique for deep networks and propose different design
strategies to address the accuracy-complexity trade-off. We carry out an
extensive comparative study between the introduced ECOC designs and the
state-of-the-art ensemble techniques such as ensemble averaging and gradient
boosting decision trees. Furthermore, we propose a combinatory technique which
is shown to achieve the highest classification performance amongst all.

33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph