NDLI: Algorithms for Fast Large Scale Data Mining Using Logistic Regression

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2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

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2007 IEEE Symposium on Computational Intelligence and Data Mining

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007)

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Algorithms for Fast Large Scale Data Mining Using Logistic Regression

Content Provider	IEEE Xplore Digital Library
Author	Rouhani-Kalleh, O.
Copyright Year	2007
Description	Author affiliation: Microsoft, Redmond, WA (Rouhani-Kalleh, O.)
Abstract	This paper proposes two new efficient algorithms to train logistic regression classifiers using very large data sets. Our algorithms will lower the upper bound time complexity that the existing algorithm in the literature has and our experiments confirm that our proposed algorithms significantly improve the execution time. For our data sets, which come from Microsoft's Web logs, the execution time was reduced up to 353 times as compared to the algorithm often referenced in the literature. The improvement will be even greater for larger data sets
Starting Page	155
Ending Page	162
File Size	7691613
Page Count	8
File Format	PDF
ISBN	1424407052
DOI	10.1109/CIDM.2007.368867
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Upper bound USA Councils Large-scale systems Classification algorithms Data mining Proposals Equations Computational intelligence Least squares methods Logistics
Content Type	Text
Resource Type	Article

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