title: Learning and generalization with Minimerror, a temperature-dependent learning algorithm.
abstract: We study the numerical performances of Minimerror, a recently introduced learning algorithm for the perceptron that has analytically been shown to be optimal both on learning linearly and nonlinearly separable functions. We present its implementation on learning linearly separable boolean functions. Numerical results are in excellent agreement with the theoretical predictions.
authors.affiliation: CEA/Département de Recherche Fondamentale sur la Matière Condensée, SPSMS/MDN, Centre d'Etudes Nucléaires de Grenoble, France.
authors.forename: B
authors.initials: B
authors.lastname: Raffin
authors.forename: M B
authors.initials: MB
authors.lastname: Gordon
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013696:Temperature