Christopher R. Ratto, Ph.D.
Affiliations: | 2012 | Electrical and Computer Engineering | Duke University, Durham, NC |
Area:
Electronics and Electrical Engineering, StatisticsGoogle:
"Christopher Ratto"Parents
Sign in to add mentor| Leslie M. Collins | grad student | 2012 | Duke | |
| (Nonparametric Bayesian context learning for buried threat detection.) | ||||
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Publications
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| Caceres CA, Roos MJ, Rupp KM, et al. (2017) Feature Selection Methods for Zero-Shot Learning of Neural Activity. Frontiers in Neuroinformatics. 11: 41 |
| Rupp K, Roos M, Milsap G, et al. (2017) Semantic attributes are encoded in human electrocorticographic signals during visual object recognition. Neuroimage |
| Ratto CR, Caceres CA, Schoeberlein HC. (2015) Cost-Constrained Feature Optimization in Kernel Machine Classifiers Ieee Signal Processing Letters. 22: 2469-2473 |
| Ratto CR, Jr KDM, Collins LM, et al. (2014) Bayesian context-dependent learning for anomaly classification in hyperspectral imagery Ieee Transactions On Geoscience and Remote Sensing. 52: 1969-1981 |
| Ratto CR, Morton KD, Collins LM, et al. (2014) Analysis of linear prediction for soil characterization in gpr data for countermine applications Sensing and Imaging. 15 |
| Ratto CR, Morton KD, McMichael IT, et al. (2012) Integration of lidar with the NIITEK GPR for improved performance on rough terrain Proceedings of Spie - the International Society For Optical Engineering. 8357 |
| Ratto CR, Morton KD, Collins LM, et al. (2012) A Bayesian method for discriminative context-dependent fusion of GPR-based detection algorithms Proceedings of Spie - the International Society For Optical Engineering. 8357 |
| Ratto CR, Morton KD, Collins LM, et al. (2011) Contextual learning in ground-penetrating radar data using Dirichlet process priors Proceedings of Spie - the International Society For Optical Engineering. 8017 |
| Ratto CR, Morton KD, Collins LM, et al. (2011) Physics-based features for identifying contextual factors affecting landmine detection with ground-penetrating radar Proceedings of Spie - the International Society For Optical Engineering. 8017 |
| Ratto CR, Morton KD, Collins LM, et al. (2011) A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification Workshop On Hyperspectral Image and Signal Processing, Evolution in Remote Sensing |