Аннотация:
Object recognition in images is used in many areas
of practical use. Very often, progress in its application
largely depends on the ratio of the quality of object
recognition and the required amount of calculations.
Recent advances in recognition are related to the
development of neural network architectures with a
very significant amount of computing that are trained
on large data sets over a very long time on state-ofthe-art computers. For many practical applications,
it is not possible to collect such large datasets for
training and only computing machines with limited
computing power can be used. Therefore, the search
for solutions that meet these practical restrictions is
relevant. This paper reports an ensemble classifier,
which uses stacking in the second stage. The use of
significantly different classifiers in the first stage and
the multilayer perceptron in the second stage has
made it possible to significantly improve the ratio of
the quality of classification and the required volume
of calculations when training on small data sets. The
current study showed that the use of a multilayer
perceptron in the second stage makes it possible to
reduce the error compared to the use of the second
stage of majority voting. On the MNIST dataset, the
error reduction was 29‒39 %. On the CIFAR-10 dataset,
the error reduction was 13‒17 %. A comparison of
the proposed architecture of the ensemble classifier
with the architecture of the transformer-type classifier
demonstrated a decrease in the volume of calculations
while reducing the error. For the CIFAR-10 dataset, an
error reduction of 8 % was achieved with a calculation
volume of less than 22 times. For the MNIST dataset,
the error reduction was 62 % when winning by the
volume of calculations by 50 times
