Fyfe, C. and Gabrys, B., 1999. E-insensitive Unsupervised Learning. In: International Conference on Neural Networks and Artificial Intelligence (ICNNAI'99), October 1999, Brest, Belarus, pp. 10-18.
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One of the major paradigms for unsupervised learning in Artificial Neural Networks is Hebbian learning. The standard implementations of Hebbian learning are optimal under the assumptions of Gaussian noise in a data set. We derive e-insensitive Hebbian learning based on minimising the least absolute error in a compressed data set and show that the learning rule is equivalent to the Principal Component Analysis (PCA) networks' learning rules under a variety of conditions.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||School of Design, Engineering & Computing|
|Deposited By:||Professor Bogdan Gabrys|
|Deposited On:||11 Mar 2009 21:36|
|Last Modified:||07 Mar 2013 15:07|
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