Designing Kernel Scheme for Classifiers Fusion

نویسنده:
, , , , , , ,
سال
: 2009
چکیده: 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.
کلیدواژه(گان): classifiers fusion,combining classifiers,NN

classifiers,kernel methods,least mean square
کالکشن :
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