  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.

Machine Learning;Machine Learning