Abstract
LIPNet is a pyramidal neural network with lateral inhibition developed for pattern recognition, inspired in the concept of receptive and inhibitory fields from the human visual system. Although this network can implicitly extract features and use these features to properly classify patterns in images, many parameters must be defined prior to the network training and operation. Besides, these parameters have a huge impact on the recognition performance. This paper proposes an encoding scheme aiming at optimizing the LIPNet structure using Particle Swarm Optimization. Preliminary results for a face detection problem using a well known benchmark set showed that our approach achieved better classification rates when compared to the original LIPNet.
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Soares, A.M., Fernandes, B.J.T., Bastos-Filho, C.J.A. (2014). Lateral Inhibition Pyramidal Neural Networks Designed by Particle Swarm Optimization. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_84
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DOI: https://doi.org/10.1007/978-3-319-11179-7_84
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