Distribution theory based enrichment of sparse data for...

Details Distribution theory based enrichment of sparse data for... Distribution theory based enrichment of sparse data for...

2006-10-24

2006-10-24

Knight, Anthony (Department: 2121)

Data processing: artificial intelligence

Machine learning

C382S133000

Reexamination Certificate

active

07127435

ABSTRACT:
A technique for enriching sparse data for machine learning techniques such as supervised artificial neural network includes receiving the sparse data and enriching the received data around a deviation of the mean of the received data using a predetermined distribution. The technique further includes outputting the enriched data for unbiased and increased performance during the machine learning.

REFERENCES:
patent: 5359699 (1994-10-01), Tong et al.
patent: 6735578 (2004-05-01), Shetty et al.
Mario A.T. Figueiredo, “Adaptive Sparseness for Supervised Learning”.
B.D. Ripley, “Pattern Recognition via Neural Networks”.
Lars Kai Hansen and Jan Larsen, “Unsupervised Learning and Generalization”.
Kevin Swingler, “Applying Neural Networks—A Practical Guide” Academic Press, 1996.
Jürgen Van Gorp et al., An Interpolation Technique for Learning With Sparse Data, 2000, SYSID 2000.
D. Jakominich et al., Real Time Digital Power System Simulator Design Consideration And Relay Performance Evaluation, 1995, ICDS'95.
Hassoun, Fundamental of Artificial Neural Networks, 1995, The MIT Press.

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