The eigenvalues of the Dirichlet Laplacian are used to generate three different sets of features for shape recognition and classification in binary images. The generated feature...
Mohamed A. Khabou, Lotfi Hermi, Mohamed Ben Hadj R...
We report new results on the corner classification approach to training feedforward neural networks. It is shown that a prescriptive learning procedure where the weights are simp...
This paper introduces a learning method for two-layer feedforward neural networks based on sensitivity analysis, which uses a linear training algorithm for each of the two layers....
We investigate the effectiveness of GP-generated intelligent structures in classification tasks. Specifically, we present and use four context-free grammars to describe (1) decisi...
Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the class...
Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application ...
For many types of machine learning algorithms, one can compute the statistically optimal" way to select training data. In this paper, we review how optimal data selection tec...
David A. Cohn, Zoubin Ghahramani, Michael I. Jorda...
A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each networ...
Several recent publications have exhibited relationships between the theories of logic programming and of neural networks. We consider a general approach to representing normal lo...
In this work a hybrid training scheme for the supervised learning of feedforward neural networks is presented. In the proposed method, the weights of the last layer are obtained em...