In this paper, a new classifier, called adaptive high order neural tree (AHNT), is proposed for pattern recognition applications. It is a hierarchical multi-level neural network, in which the nodes are organized into a tree topology. It successively partitions the training set into subsets, assigning each subset to a different child node. Each node can be a first-order or a high order perceptro...

Abstract Within the framework of on-line learning, we study the generalization error of an ensemble learning machine learning from a linear teacher perceptron. The generalization error achieved by an ensemble of linear perceptrons having homogeneous or inhomogeneous initial weight vectors is precisely calculated at the thermodynamic limit of a large number of input elements and shows rich behav...

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study ac...

RÉSUMÉ. Les perceptrons multi-couches (ou perceptrons) sont largement utilisés pour l’approximation de fonctions, mais sont très demandeursen temps de calcul. Malheureusement, l’implantation parallèle des perceptrons est un problème complexe ; dans cet article, nous proposons une nouvelle méthode efficace pour leur parallélisation. Nous présentons l’architecture OWE (Orthogonal Weight Estimator...

High order perceptrons are often used in order to reduce the size of neural networks The complexity of the architecture of a usual multilayer network is then turned into the complexity of the functions performed by each high order unit and in particular by the degree of their polynomials The main result of this paper provides a bound on the degree of the polynomial of a high order perceptron wh...