- L2 Kernel Classification JooSeuk Kim, Student Member, IEEE, and Clayton D. Scott, Member, IEEE
- Learning Minimum Volume Sets Clayton Scott
- KERNEL CLASSIFICATION VIA INTEGRATED SQUARED ERROR JooSeuk Kim and Clayton D. Scott
- Supplementary Material Gowtham Bellala
- Learning Minimum Volume Sets Clayton Scott # and Robert Nowak +
- Electronic Journal of Statistics Vol. 3 (2009) 651677
- 3806 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 11, NOVEMBER 2005 A NeymanPearson Approach to Statistical Learning
- On the Robustness of Kernel Density M-Estimators JooSeuk Kim stannum@umich.edu
- Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs
- Temporal Features and Kernel Methods for Predicting Sepsis in Postoperative Patients
- Benefits of PositionSensitive Detectors for Radioactive Source Detection
- Nonparametric Assessment of Contamination in Multivariate Data Using Generalized Quantile Sets and FDR
- Nested Support Vector Machines Gyemin Lee, Student Member, IEEE, and Clayton Scott, Member, IEEE
- The Annals of Statistics 2009, Vol. 37, No. 5B, 27602782
- Journal of Machine Learning Research 7 (2006) 665704 Submitted 9/05; Published 4/06 Learning Minimum Volume Sets
- 1 Supplementary Material: Complete Proof of Theorem 4 Define two new functions L and H as
- Performance analysis for L2 kernel classification JooSeuk Kim
- NESTED SUPPORT VECTOR MACHINES Gyemin Lee and Clayton Scott
- ANNOTATED MINIMUM VOLUME SETS FOR NONPARAMETRIC ANOMALY DISCOVERY
- LEARNING MINIMUM VOLUME SETS WITH SUPPORT VECTOR MACHINES Mark A. Davenport, Richard G. Baraniuk
- On the Adaptive Properties of Decision Trees Clayton Scott
- Dyadic Classification Trees Structural Risk Minimization
- A Novel System for Rapidly Identifying Toxic Chemicals Suresh K. Bhavnani1,2
- Learning Minimum Volume Sets Clayton Scott
- Transfer Learning for Automatic Gating of Flow Cytometry Data Gyemin Lee
- Template Learning from Atomic Representations: A Wavelet-based
- 4518 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 12, DECEMBER 2005 Tree Pruning With Subadditive Penalties
- 1648 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 3, MARCH 2010 Nested Support Vector Machines
- IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 52, NO. 4, APRIL 2006 1335 Minimax-Optimal Classification With Dyadic
- Appendix to Nonparametric Assessment of Contamination in Multivariate Data Using Generalized Quantile Sets and FDR,
- On the Adaptive Properties of Decision Trees Clayton Scott
- Nonparametric Assessment of Contamination in Multivariate Data Using Minimum Volume Sets and FDR
- MEAN VALUES OF DEDEKIND SUMS J. B. Conrey
- Complexity Regularized Dyadic Classi cation Trees: Ecient Pruning and Rates of Convergence
- Adaptive Hausdorff Estimation of Density Level Sets Aarti Singh and Robert D. Nowak
- Novelty detection: Unlabeled data definitely help Clayton Scott
- Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs
- Learning Minimum Volume Sets Clayton Scott
- 2264 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 52, NO. 8, AUGUST 2004 TEMPLAR: A Wavelet-Based Framework for Pattern
- CONTROLLING FALSE ALARMS WITH SUPPORT VECTOR MACHINES Mark A. Davenport, Richard G. Baraniuk
- EM algorithms for multivariate Gaussian mixture models with truncated and censored data
- Dyadic Classification Trees Structural Risk Minimization
- THE ONE CLASS SUPPORT VECTOR MACHINE SOLUTION PATH Gyemin Lee and Clayton D. Scott
- GENERALIZATION ERROR ANALYSIS FOR FDR CONTROLLED CLASSIFICATION Clayton Scott, Gowtham Bellala
- ROBUST KERNEL DENSITY ESTIMATION JooSeuk Kim and Clayton Scott
- IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 7, JULY 2006 1831 Robust Contour Matching Via
- NearMinimax Optimal Classification with Dyadic Classification Trees
- arXiv:submit/0108637[stat.ML]14Sep2010 Calibrated Surrogate Losses for Classification with
- Transductive anomaly detection Clayton Scott and Gilles Blanchard
- Journal of Machine Learning Research 11 (2010) 2973-3009 Submitted 7/09; Revised 5/10; Published 11/10 Semi-Supervised Novelty Detection
- MINIMAX SUPPORT VECTOR MACHINES Mark A. Davenport, Richard G. Baraniuk
- CORT: CLASSIFICATION OR REGRESSION TREES Clayton D. Scott, Rebecca M. Willett and Robert D. Nowak
- Supplemental: Active Diagnosis via AUC Maximization Gowtham Bellala, Jason Stanley, Clayton Scott
- Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
