Making Logistic Regression a Core Data Mining Tool with TR-IRLS Export

@inproceedings{citeulike:4207559, abstract = {Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiersare accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these requirements. A combination of regularization, truncated Newton methods, and iteratively re-weighted least squares make it faster and more accurate than modern SVM implementations, and relatively insensitive to parameters. It is robust to linear dependencies and some scaling problems, making most data preprocessing unnecessary.}, address = {Washington, DC, USA}, author = {Komarek, Paul and Moore, Andrew W.}, booktitle = {ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining}, citeulike-article-id = {4207559}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1106426}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/ICDM.2005.90}, doi = {10.1109/ICDM.2005.90}, isbn = {0-7695-2278-5}, keywords = {logisticregression}, pages = {685--688}, posted-at = {2009-03-22 19:05:47}, priority = {0}, publisher = {IEEE Computer Society}, title = {Making Logistic Regression a Core Data Mining Tool with TR-IRLS}, url = {http://dx.doi.org/10.1109/ICDM.2005.90}, year = {2005} }

TY - CONF ID - citeulike:4207559 L3 - citeulike-article-id:4207559 N2 - Binary classification is a core data mining task. For large datasets or real-time applications, desirable classifiersare accurate, fast, and need no parameter tuning. We present a simple implementation of logistic regression that meets these requirements. A combination of regularization, truncated Newton methods, and iteratively re-weighted least squares make it faster and more accurate than modern SVM implementations, and relatively insensitive to parameters. It is robust to linear dependencies and some scaling problems, making most data preprocessing unnecessary. SN - 0-7695-2278-5 AD - Washington, DC, USA EP - 688 TI - Making Logistic Regression a Core Data Mining Tool with TR-IRLS PB - IEEE Computer Society T2 - ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining SP - 685 KW - logisticregression AU - Komarek, Paul AU - Moore, Andrew PY - 2005/// UR - http://dx.doi.org/10.1109/ICDM.2005.90 ER -