NDLI: Fusion Algorithm for Locally Arranged Linear Models

Content Provider	IEEE Xplore Digital Library
Author	Hoppe, F. Sommer, G.
Copyright Year	2006
Description	Author affiliation: Cognitive Syst. Group, Christian-Albrechts-Univ., Kiel (Hoppe, F.; Sommer, G.)
Abstract	As an extension to a recently proposed local linear approximation method we present an algorithm that generates more compact solutions for supervised-learning problems. Given a network of linear models each trained to approximate the target function in a local region of the input space, the algorithm reduces the number of the models significantly without diminishing the accuracy of the approximation. It fuses linear models by combining their local regions of validity to more complex, non-symmetrically shaped ones. A neighborhood graph introducing edges in a purely data-driven manner between adjacent linear models is used to determine which models should be fused. The also extended model for a region of validity allows to detect automatically data which is novel to a trained network and should be regarded as an outlier. The effectiveness of the proposed methods is shown with a benchmark test achieving a five times smaller RMSE than the best competitors
Sponsorship	IEEE CPS
Starting Page	951
Ending Page	951
File Size	156651
Page Count	1
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear approximation Approximation algorithms Vectors Fusion power generation Fuses Benchmark testing System testing Machine learning Control system synthesis Control theory
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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