Abstract
We develop artificial neural networks which extract structure from visual data. We explore an extension of Hebbian Learning which has been called ɛ- Insensitive Hebbian Learning and show that it may be thought of as a special case of Maximum Likelihood Hebbian learning and investigate the resulting network with both real and artificial data. We show that the resulting network is able to identify a single orientation of bars from a mixture of horizontal and vertical bars and also it is able to identify local filters from video images.
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Corchado, E., Fyfe, C. (2002). Identification of Visual Features Using a Neural Version of Exploratory Projection Pursuit. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_19
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DOI: https://doi.org/10.1007/3-540-45750-X_19
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