Show simple item record

dc.contributor.authorMcDowall, Thomas M.
dc.contributor.authorHam, Fredric
dc.date.accessioned2017-10-05T13:11:43Z
dc.date.available2017-10-05T13:11:43Z
dc.date.issued1997-04-04
dc.identifier.citationMcDowall, T. M., & Ham, F. M. (1997). Robust partial least-squares regression: A modular neural network approach. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, , 3077 344-355.en_US
dc.identifier.urihttp://hdl.handle.net/11141/1751
dc.descriptionCalibration, Error analysis, Learning systems, Least squares approximations, Neural networks, Regression analysis, Statistical methodsen_US
dc.description.abstractWe have developed a robust Partial Least-Squares Regression (PLSR) neural network approach to statistical calibration model development. Generalized neural network learning rules derived from a weighted statistical representation error criterion that grows less than quadratically are presented. This optimization criterion allows for higher-order statistics associated with the inputs to be taken into account and also serves to robustify the results when the empirical data contains impulsive and colored noise and outliers. The learning rules presented are considered generalized because they can be used to implement several specialized cases including: robust PLSR, linear PLSR, weighted least-squares, and variance scaling. The same learning rules also implement steepest descent or Newton's method. Newton's method can be used to formulate an adaptive learning rate for training the network.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleRobust partial least-squares regression: a modular neural network approachen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.271496


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record