@inproceedings{7e5ab371af7c4953b556d45e3fe7ec2d,
title = "The evaluation of feature extraction criteria applied to neural network classifiers",
abstract = "Feature extraction is a crucial part of classijicationprocedures. In this paper we present an approach, how to utilize feature extraction criteria to predict the potential efficiency of a neural network classijiel: Statistical and geometrical criteria are introduced for analysis. The complete system of our research consists of a class of generalized Hough-Transformations for feature extraction and a subsequent neural network. The neural network performs the classijication based on respective features. For an example we concentrated on a pattern recognition problem - The classijication of handwritten numerals. As a result of our work we assign two feature extraction criteria to the employed network for a significant estimation of its eflciency.",
keywords = "Classijication, Feature extraction, Statistical criteria",
author = "W. Utschick and P. Nachbar and C. Knobloch and A. Schuler and Nossek, {J. A.}",
note = "Publisher Copyright: {\textcopyright} 1995 IEEE.; 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 ; Conference date: 14-08-1995 Through 16-08-1995",
year = "1995",
doi = "10.1109/ICDAR.1995.599002",
language = "English",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
publisher = "IEEE Computer Society",
pages = "315--318",
booktitle = "Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995",
}