Nonlinear Classification using Ensemble of Linear Perceptrons
نویسندگان
چکیده
In this study we introduce a neural network ensemble composed of several linear perceptrons, to be used as a classifier that can rapidly be trained and effectively deals with nonlinear problems. Although each member of the ensemble can only deal with linear classification problems, through a competitive training mechanism, the ensemble is able to automatically allocate a part of the learning space that is linearly separable to each member, thus decomposing non-linear classification problems into several more manageable linear problems. Each member is equipped with an additional output neuron that produces a “confidence” value indicating the reliability of that member with regard to a given input. In the classification task, the confidence values of the ensemble’s members are used to decide a final output for the ensemble. We believe that the ability of the ensemble can improve the performance of general classifiers. Keywords—Neural Network Ensemble, Linear Perceptrons, Competitive Learning
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