An assembly neural network for texture segmentation
Neural Networks
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Flat image recognition in the process of microdevice assembly
Pattern Recognition Letters
Modular neural networks with Hebbian learning rule
Neurocomputing
Neural Networks and Micromechanics
Neural Networks and Micromechanics
The evolution of modular artificial neural networks for legged robot control
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Automatic design of modular neural networks using genetic programming
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Improved neural classifier for microscrew shape recognition
Optical Memory and Neural Networks
FPGA realization of the LIRA neural classifier
Optical Memory and Neural Networks
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better.