title: On the learning potential of the approximated quantron.
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.
authors.affiliation: Department 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
authors.forename: Richard
authors.initials: R
authors.lastname: Labib
authors.forename: Simon
authors.initials: S
authors.lastname: De Montigny
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*