  In this paper, we propose a special fusion method for combining ensembles of
base classifiers utilizing new neural networks in order to improve overall
efficiency of classification. While ensembles are designed such that each
classifier is trained independently while the decision fusion is performed as a
final procedure, in this method, we would be interested in making the fusion
process more adaptive and efficient. This new combiner, called Neural Network
Kernel Least Mean Square1, attempts to fuse outputs of the ensembles of
classifiers. The proposed Neural Network has some special properties such as
Kernel abilities,Least Mean Square features, easy learning over variants of
patterns and traditional neuron capabilities. Neural Network Kernel Least Mean
Square is a special neuron which is trained with Kernel Least Mean Square
properties. This new neuron is used as a classifiers combiner to fuse outputs
of base neural network classifiers. Performance of this method is analyzed and
compared with other fusion methods. The analysis represents higher performance
of our new method as opposed to others.
