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In the first paper of this series, a variety of known and new symmetric and weighted least-squares regression methods were presented with efficient derivations. This paper continues and generalizes the previous work with a theory for deriving, analyzing, and classifying all symmetric and weighted least-squares regression methods.


This work was originally published in Proceedings of the 1st International Conference on Computational Science and Engineering (CSE '13).


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