William Philip Kegelmeyer
William Philip Kegelmeyer
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Cited by
SMOTE: synthetic minority over-sampling technique
NV Chawla, KW Bowyer, LO Hall, WP Kegelmeyer
Journal of artificial intelligence research 16, 321-357, 2002
Current status of the digital database for screening mammography
M Heath, K Bowyer, D Kopans, P Kegelmeyer Jr, R Moore, K Chang, ...
Digital Mammography: Nijmegen, 1998, 457-460, 1998
Combination of multiple classifiers using local accuracy estimates
K Woods, WP Kegelmeyer, K Bowyer
IEEE transactions on pattern analysis and machine intelligence 19 (4), 405-410, 1997
Exploring topic coherence over many models and many topics
K Stevens, P Kegelmeyer, D Andrzejewski, D Buttler
Proceedings of the 2012 joint conference on empirical methods in natural …, 2012
A comparison of decision tree ensemble creation techniques
RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer
IEEE transactions on pattern analysis and machine intelligence 29 (1), 173-180, 2006
Computer-aided mammographic screening for spiculated lesions.
WP Kegelmeyer Jr, JM Pruneda, PD Bourland, A Hillis, MW Riggs, ...
Radiology 191 (2), 331-337, 1994
Ensemble diversity measures and their application to thinning
RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer
Information Fusion 6 (1), 49-62, 2005
Hellinger distance decision trees are robust and skew-insensitive
DA Cieslak, TR Hoens, NV Chawla, WP Kegelmeyer
Data Mining and Knowledge Discovery 24, 136-158, 2012
Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography
KS Woods, CC Doss, KW Bowyer, JL Solka, CE Priebe, ...
International Journal of Pattern Recognition and Artificial Intelligence 7 …, 1993
Data mining for scientific and engineering applications
RL Grossman, C Kamath, P Kegelmeyer, V Kumar, R Namburu
Springer Science & Business Media, 2013
Learning ensembles from bites: A scalable and accurate approach
NV Chawla, LO Hall, KW Bowyer, WP Kegelmeyer
The Journal of Machine Learning Research 5, 421-451, 2004
A new ensemble diversity measure applied to thinning ensembles
RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer
Multiple Classifier Systems: 4th International Workshop, MCS 2003 Guildford …, 2003
Multilinear algebra for analyzing data with multiple linkages
DM Dunlavy, TG Kolda, WP Kegelmeyer
Graph algorithms in the language of linear algebra, 85-114, 2011
Method and apparatus for detecting a desired behavior in digital image data
WP Kegelmeyer Jr
US Patent 5,633,948, 1997
Computer detection of stellate lesions in mammograms
WP Kegelmeyer Jr
Biomedical Image Processing and Three-Dimensional Microscopy 1660, 446-454, 1992
Distributed learning with bagging-like performance
NV Chawla, TE Moore, LO Hall, KW Bowyer, WP Kegelmeyer, C Springer
Pattern recognition letters 24 (1-3), 455-471, 2003
A comparison of ensemble creation techniques
RE Banfield, LO Hall, KW Bowyer, D Bhadoria, WP Kegelmeyer, ...
International Workshop on Multiple Classifier Systems, 223-232, 2004
The computation of cloud-base height from paired whole-sky imaging cameras
MC Allmen, WP Kegelmeyer Jr
Journal of Atmospheric and Oceanic Technology 13 (1), 97-113, 1996
PostDOCK: a structural, empirical approach to scoring protein ligand complexes
C Springer, H Adalsteinsson, MM Young, PW Kegelmeyer, DC Roe
Journal of medicinal chemistry 48 (22), 6821-6831, 2005
Comet: A recipe for learning and using large ensembles on massive data
JD Basilico, MA Munson, TG Kolda, KR Dixon, WP Kegelmeyer
2011 IEEE 11th international conference on data mining, 41-50, 2011
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