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On the learning potential of the approximated quantron
Richard Labib1, Simon De Montigny
1Department of Mathematics and Industrial Engineering, Polytechnique Montreal, 2900 boul. Édouard-Montpetit, Campus de l'Université de Montréal, 2500 Chemin de Polytechnique, Montreal, Quebec, H3T 1J4, Canada. richard.labib@polymtl.ca
Abstract:
The quantron is a hybrid neuron model related to perceptrons and spiking neurons. The activation of the quantron is determined by the maximum of a sum of input signals, which is difficult to use in classical learning algorithms. Thus, training the quantron to solve classification problems requires heuristic methods such as direct search. In this paper, we present an approximation of the quantron trainable by gradient search. We show this approximation improves the classification performance of direct search solutions. We also compare the quantron and the perceptron's performance in solving the IRIS classification problem.
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Accuracy, limits, and approximation
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...