Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ensemble of Binary Classifiers Combined Using Recurrent Correlation
  Associative Memories   An ensemble method should cleverly combine a group of base classifiers to
yield an improved classifier. The majority vote is an example of a methodology
used to combine classifiers in an ensemble method. In this paper, we propose to
combine classifiers using an associative memory model. Precisely, we introduce
ensemble methods based on recurrent correlation associative memories (RCAMs)
for binary classification problems. We show that an RCAM-based ensemble
classifier can be viewed as a majority vote classifier whose weights depend on
the similarity between the base classifiers and the resulting ensemble method.
More precisely, the RCAM-based ensemble combines the classifiers using a
recurrent consult and vote scheme. Furthermore, computational experiments
confirm the potential application of the RCAM-based ensemble method for binary
classification problems.

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The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat