Distributed Learning on Very Large Data Sets (2000)

by Lawrence O. Hall , Kevin W. Bowyer , W. Philip Kegelmeyer , Thomas E. Moore , Jr. , Chi-ming Chao
Venue:In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Citations:11 - 4 self

Active Bibliography

RIDE: Rule-Learning in a Distributed Environment – Nitesh Chawla - 2000
The DOE’S Accelerated Strategic Computing Initiative – Kevin W. Bowyer, Lawrence Hall, Thomas Moore, Nitesh Chawla, W. Philip Kegelmeyer
11 Decision tree learning on very large data sets – Lawrence O. Hall, Nitesh Chawla, Kevin W. Bowyer - 1998
32 Learning ensembles from bites: A scalable and accurate approach – Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
301 MetaCost: A General Method for Making Classifiers Cost-Sensitive – Pedro Domingos - 1999
62 Tree induction vs. logistic regression: A learning-curve analysis – Claudia Perlich, Foster Provost, Jeffrey S. Simonoff - 2001
Combining Decision Trees Learned in Parallel – Lawrence Hall Nitesh, Lawrence O. Hall, Nitesh Chawla, Kevin W. Bowyer - 1998
4 Tell me who can learn you and I can tell you who you are: Landmarking Various Learning Learning Algorithms – Bernhard Pfahringer, Hilan Bensusan, CHristophe Giraud-Carrier - 2000
80 Meta-Learning in Distributed Data Mining Systems: Issues and Approaches – Andreas L. Prodromidis, Philip K. Chan, Salvatore J. Stolfo - 2000
2 Bagging-Like Effects for Decision Trees and Neural Nets in Protein Secondary Structure Prediction – Nitesh Chawla, Thomas E. Moore, Jr., Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, Philip Kegelmeyer - 2001
www.elsevier.com/locate/patrec Distributed learning with bagging-like performance – Nitesh V. Chawla A, Thomas E. Moore A, Lawrence O. Hall A, Kevin W. Bowyer B, W. Philip Kegelmeyer C, Clayton Springer C - 2002
14 Distributed Learning with Bagging-Like Performance – Nitesh Chawla , Thomas E. Moore, Jr., Lawrence O. Hall, Kevin W. Bowyer, Philip Kegelmeyer, Clayton Springer - 2003
14 Distributed Data Mining: Scaling up and beyond – Foster Provost - 1999
82 The Effect of Class Distribution on Classifier Learning: An Empirical Study – Gary Weiss, Foster Provost - 2001
564 A Short Introduction to Boosting – Yoav Freund, Robert E. Schapire - 1999
16 Learning Rules from Distributed Data – Lawrence O. Hall, Nitesh Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer, E. Fowler Ave
93 A simple, fast, and effective rule learner – William W. Cohen, Yoram Singer - 1999
25 Combining Classifiers with Meta Decision Trees – Ljupco Todorovski, Saso Dzeroski - 2003
112 A streaming ensemble algorithm (SEA) for large-scale classification – W. Nick Street - 2001