Title :
Analysis of non-Gaussian data using a neural network
Author :
Gong, Wenyu ; Manry, Michael T.
Author_Institution :
Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA
Abstract :
Summary form only given, as follows. A neural net classifier is applied to non-Gaussian features calculated from numeric hand-printed (NHP) characters. A topological classifier is applied to the same data for comparison. Others have shown that neural nets can be optimal. A neural net is used to verify that the performance of the topological classifier is near optimal. A feature selection approach which utilizes the neural net is proposed and demonstrated as well as a method for fast learning. A reject category is developed so that bad characters are not classified, and the neural net is allowed to express uncertainty.<>
Keywords :
character recognition; learning systems; neural nets; character recognition; fast learning; feature selection; neural net classifier; neural network; nonGaussian data; numeric hand-printed; reject category; topological classifier; uncertainty; Character recognition; Learning systems; Neural networks;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118318
