Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
Multi-agent neural business control system
Information Sciences: an International Journal
Maximum likelihood hebbian learning based Retrieval method for CBR systems
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Neural business control system
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
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We investigate an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function(pdf). We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors [5] have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit(EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.