Mehmet M. Dundar, Ph.D.
Affiliations: | 2003 | Purdue University, West Lafayette, IN, United States |
Area:
Electronics and Electrical Engineering, Computer ScienceGoogle:
"Mehmet Dundar"Parents
Sign in to add mentorDavid A. Landgrebe | grad student | 2003 | Purdue | |
(Toward an optimal analysis of hyperspectral data.) |
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Publications
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Dundar MM, Badve S, Raykar VC, et al. (2010) A multiple instance learning approach toward optimal classification of pathology slides Proceedings - International Conference On Pattern Recognition. 2732-2735 |
Dundar MM, Hirleman ED, Bhunia AK, et al. (2009) Learning with a non-exhaustive training dataset a case study: Detection of bacteria cultures using optical-scattering technology Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 279-287 |
Akova F, Hirleman D, Bhunia AK, et al. (2009) Non-exhaustive learning for bacteria detection Nbis 2009 - 12th International Conference On Network-Based Information Systems. 206-211 |
Dundar MM. (2009) Target detection with spatio-spectral data via concordance learning International Geoscience and Remote Sensing Symposium (Igarss). 2 |
Dundar MM, Wolf M, Lakare S, et al. (2008) Polyhedral classifier for target detection a case study: Colorectal cancer Proceedings of the 25th International Conference On Machine Learning. 288-295 |
Dundar MM, Bi J. (2007) Joint optimization of cascaded classifiers for computer aided detection Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition |
Dundar MM, Landgrebe D. (2004) A kernel-based supervised classifier for the analysis of hyperspectral data 2003 Ieee Workshop On Advances in Techniques For Analysis of Remotely Sensed Data. 320-326 |
Dundar MM, Landgrebe DA. (2004) A cost-effective semisupervised classifier approach with kernels Ieee Transactions On Geoscience and Remote Sensing. 42: 264-270 |
Dundar MM, Landgrebe DA. (2004) Toward an optimal supervised classifier for the analysis of hyperspectral data Ieee Transactions On Geoscience and Remote Sensing. 42: 271-277 |