Dan Margolis
Dan Margolis
Dirección de correo verificada de binghamton.edu
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Kernelized partial least squares for feature reduction and classification of gene microarray data
WH Land, X Qiao, DE Margolis, WS Ford, CT Paquette, JF Perez-Rogers, ...
BMC systems biology 5 (S3), S13, 2011
112011
A kernel approach for ensemble decision combinations with two-view mammography applications
WH Land Jr, D Margolis, M Kallergi, JJ Heine
International Journal of Functional Informatics and Personalised Medicine 3 …, 2010
92010
Systems and methods for tissue classification using attributes of a biomarker enhanced tissue network (BETN)
A Santamaria-Pang, DE Margolis
US Patent 8,831,327, 2014
82014
Investigating the GRNN Oracle as a method for combining multiple predictive models of colon cancer recurrence from gene microarrays
AS Campbell, WH Land, D Margolis, R Mathur, D Schaffer
Procedia Computer Science 20, 374-378, 2013
52013
Tissue segmentation and classification using graph-based unsupervised clustering
D Margolis, A Santamaria-Pang, J Rittscher
2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 162-165, 2012
52012
A complex adaptive system using statistical learning theory as an inline preprocess for clinical survival analysis
D Margolis, WH Land Jr, R Gottlieb, X Qiao
Procedia Computer Science 6, 279-284, 2011
52011
Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory
WH Land, D Margolis, R Gottlieb, EA Krupinski, JY Yang
BMC genomics 11 (S3), S15, 2010
52010
A new tool for survival analysis: evolutionary programming/evolutionary strategies (EP/ES) support vector regression hybrid using both censored/non-censored (event) data
WH Land Jr, X Qiao, D Margolis, R Gottlieb
Procedia Computer Science 6, 267-272, 2011
22011
Crowdsourcing with complex workers: Utilizing prior knowledge of worker experience and active learning
DE Margolis
State University of New York at Binghamton, 2013
2013
Evaluating response to treatment and predicting outcome in patients with metastatic colorectal carcinoma using statistical learning theory
DE Margolis
State University of New York at Binghamton, 2010
2010
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Artículos 1–10