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Abstract
This paper extends a recent and very appealing approach of computational learning to the field of image analysis. Recent works have demonstrated that the implementation of Artificial Neural Networks (ANN) could be simplified by using a large amount of neurons with random weights. Only the output weights are adapted, with a single linear regression. Supervised learning is very fast and efficient. To adapt this approach to image analysis, the novelty is to initialize weights, not as independent random variables, but as Gaussian functions with only a few random parameters. This creates smooth random receptive fields in the image space. TheseImage Receptive Fields - Neural Networks (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.
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Authors and Affiliations
MIPS Laboratory, Université de Haute-Alsace, Mulhouse, France
Paméla Daum, Jean-Luc Buessler & Jean-Philippe Urban
- Paméla Daum
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- Jean-Luc Buessler
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- Jean-Philippe Urban
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Editors and Affiliations
Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland
Timo Honkela & Samuel Kaski &
School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland
Włodzisław Duch
Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK
Mark Girolami
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Daum, P., Buessler, JL., Urban, JP. (2011). Image Receptive Fields Neural Networks for Object Recognition. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_13
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