NDLI: ECOC-based training of neural networks for face recognition

Content Provider	IEEE Xplore Digital Library
Author	Hatami, N. Ebrahimpour, R. Ghaderi, R.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Mazandaran Univ., Babol (Ghaderi, R.) \|\| Dept. of Electr. Eng., Shahed Univ., Tehran (Hatami, N.) \|\| Dept. of Electr. Eng., Shahid Rajaee Univ., Tehran (Ebrahimpour, R.)
Abstract	Error correcting output codes, ECOC, is an output representation method capable of discovering some of the errors produced in classification tasks. This paper describes the application of ECOC to the training of feed forward neural networks, FFNN, for improving the overall accuracy of classification systems. Indeed, to improve the generalization of FFNN classifiers, this paper proposes an ECOC-Based training method for neural networks that use ECOC as the output representation, and adopts the traditional back-propagation algorithm, BP, to adjust weights of the network. Experimental results for face recognition problem on Yale database demonstrate the effectiveness of our method. With a rejection scheme defined by a simple robustness rate, high reliability is achieved in this application.
Starting Page	450
Ending Page	454
File Size	491207
Page Count	5
File Format	PDF
ISBN	9781424416738
DOI	10.1109/ICCIS.2008.4670763
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Face recognition MIMO Error correction codes Feeds Feedforward neural networks Backpropagation algorithms Error correction Multilayer perceptrons Hamming distance Multi-layer Perceptron Error correcting output coding Error Back-Propagation algorithm Face Recognition
Content Type	Text
Resource Type	Article

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