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ORCIDW. Philip Kegelmeyer
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Journal Articles
- 2019
[j12]Aditya Konduri
, Hemanth Kolla, W. Philip Kegelmeyer, Timothy M. Shead, Julia Ling, Warren L. Davis IV:
Anomaly detection in scientific data using joint statistical moments. J. Comput. Phys. 387: 522-538 (2019)- [j11]Connor Amorin, Laura M. Kegelmeyer, W. Philip Kegelmeyer:
A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics. Stat. Anal. Data Min. 12(6): 505-513 (2019) - 2012
- [j10]David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla, W. Philip Kegelmeyer:
Hellinger distance decision trees are robust and skew-insensitive. Data Min. Knowl. Discov. 24(1): 136-158 (2012) - 2011
- [j9]Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Detecting and ordering salient regions. Data Min. Knowl. Discov. 22(1-2): 259-290 (2011) - 2008
- [j8]Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Using classifier ensembles to label spatially disjoint data. Inf. Fusion 9(1): 120-133 (2008) - 2007
- [j7]Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Trans. Pattern Anal. Mach. Intell. 29(1): 173-180 (2007) - 2005
- [j6]Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Ensemble diversity measures and their application to thinning. Inf. Fusion 6(1): 49-62 (2005) - 2004
- [j5]Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Learning Ensembles from Bites: A Scalable and Accurate Approach. J. Mach. Learn. Res. 5: 421-451 (2004) - 2003
- [j4]Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer:
Distributed learning with bagging-like performance. Pattern Recognit. Lett. 24(1-3): 455-471 (2003) - 2002
- [j3]Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer:
SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 16: 321-357 (2002) - 1997
- [j2]Mark C. Allmen, W. Philip Kegelmeyer:
The computation of cloud base height from paired whole-sky imaging cameras. Mach. Vis. Appl. 9(4): 160-165 (1997) - [j1]Kevin S. Woods, W. Philip Kegelmeyer, Kevin W. Bowyer:
Combination of Multiple Classifiers Using Local Accuracy Estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19(4): 405-410 (1997)
Conference and Workshop Papers
- 2020
- [c27]Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Bridget I. Haus, Eva Domschot, Ramyaa, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer:
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis. AISec@CCS 2020: 49-60 - 2017
- [c26]Julia Ling, W. Philip Kegelmeyer, Aditya Konduri, Hemanth Kolla, Kevin A. Reed, Timothy M. Shead, Warren L. Davis IV:
Using feature importance metrics to detect events of interest in scientific computing applications. LDAV 2017: 55-63 - 2015
- [c25]Jonathan Crussell, W. Philip Kegelmeyer:
Attacking DBSCAN for Fun and Profit. SDM 2015: 235-243 - 2013
- [c24]W. Philip Kegelmeyer, Ken Chiang, Joey Burton Ingram:
Streaming Malware Classification in the Presence of Concept Drift and Class Imbalance. ICMLA (2) 2013: 48-53 - 2012
- [c23]Keith Stevens, W. Philip Kegelmeyer, David Andrzejewski, David Buttler:
Exploring Topic Coherence over Many Models and Many Topics. EMNLP-CoNLL 2012: 952-961 - 2011
- [c22]Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, W. Philip Kegelmeyer:
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data. ICDM 2011: 41-50 - [c21]Brett W. Bader, W. Philip Kegelmeyer, Peter A. Chew:
Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning. ICDM Workshops 2011: 45-52 - 2009
- [c20]Michael J. Procopio, W. Philip Kegelmeyer, Gregory Z. Grudic, Jane Mulligan:
Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation. MCS 2009: 385-397 - 2008
- [c19]John Nicholas Korecki, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Semi-supervised learning on large complex simulations. ICPR 2008: 1-4 - [c18]Larry Shoemaker, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Detecting and ordering salient regions for efficient browsing. ICPR 2008: 1-4 - [c17]Clayton Springer, W. Philip Kegelmeyer:
Feature selection via decision tree surrogate splits. ICPR 2008: 1-5 - 2007
- [c16]Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer:
Boosting Lite - Handling Larger Datasets and Slower Base Classifiers. MCS 2007: 161-170 - 2006
- [c15]Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Learning to Predict Salient Regions from Disjoint and Skewed Training Sets. ICTAI 2006: 116-126 - 2005
- [c14]Wendy S. Koegler, W. Philip Kegelmeyer:
FCLib: A Library for Building Data Analysis and Data Discovery Tools. IDA 2005: 192-203 - [c13]Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Ensembles of Classifiers from Spatially Disjoint Data. Multiple Classifier Systems 2005: 196-205 - 2004
- [c12]Eric T. Stanton, W. Philip Kegelmeyer:
Creating and Managing "Lookmarks" in ParaView. INFOVIS 2004 - [c11]Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich:
A Comparison of Ensemble Creation Techniques. Multiple Classifier Systems 2004: 223-232 - 2003
- [c10]Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich:
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging. ICDM 2003: 533-536 - [c9]Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
A New Ensemble Diversity Measure Applied to Thinning Ensembles. Multiple Classifier Systems 2003: 306-316 - 2002
- [c8]Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer:
Distributed Pasting of Small Votes. Multiple Classifier Systems 2002: 52-61 - 2001
- [c7]Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer:
Bagging Is a Small-Data-Set Phenomenon. CVPR (2) 2001: 684-689 - [c6]Elizabeth Bradley, Nancy Collins, W. Philip Kegelmeyer:
Feature Characterization in Scientific Datasets. IDA 2001: 1-12 - [c5]Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer:
Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction. BIOKDD 2001: 50-59 - 2000
- [c4]Kevin W. Bowyer, Lawrence O. Hall, Thomas Moore, Nitesh V. Chawla, W. Philip Kegelmeyer:
A parallel decision tree builder for mining very large visualization datasets. SMC 2000: 1888-1893 - 1999
- [c3]Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer:
Learning Rules from Distributed Data. Large-Scale Parallel Data Mining 1999: 211-220 - 1996
- [c2]Kevin S. Woods, Kevin W. Bowyer, W. Philip Kegelmeyer:
Combination of Multiple Classifiers Using Local Accuracy Estimates. CVPR 1996: 391-396 - [c1]Bennett R. Groshong, W. Philip Kegelmeyer:
Detecting circumscribed lesions with the Hough transform. Medical Imaging: Image Processing 1996
Parts in Books or Collections
- 2011
- [p1]Daniel M. Dunlavy, Tamara G. Kolda, W. Philip Kegelmeyer:
Multilinear Algebra for Analyzing Data with Multiple Linkages. Graph Algorithms in the Language of Linear Algebra 2011: 85-114
Informal and Other Publications
- 2020
- [i4]Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Ramyaa Ramyaa, Evelyn Domschot, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer:
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security. CoRR abs/2005.01800 (2020) - 2019
- [i3]Gary J. Saavedra, Kathryn N. Rodhouse, Daniel M. Dunlavy, W. Philip Kegelmeyer:
A Review of Machine Learning Applications in Fuzzing. CoRR abs/1906.11133 (2019) - 2011
- [i2]Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, W. Philip Kegelmeyer:
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data. CoRR abs/1103.2068 (2011) - [i1]Kevin W. Bowyer, Nitesh V. Chawla, Lawrence O. Hall, W. Philip Kegelmeyer:
SMOTE: Synthetic Minority Over-sampling Technique. CoRR abs/1106.1813 (2011)
Coauthor Index
aka: Larry O. Hall
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