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» Being Sensitive to Uncertainty
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ML
2008
ACM
248views Machine Learning» more  ML 2008»
13 years 8 months ago
Feature selection via sensitivity analysis of SVM probabilistic outputs
Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) l...
Kai Quan Shen, Chong Jin Ong, Xiao Ping Li, Einar ...
TITB
2008
110views more  TITB 2008»
13 years 7 months ago
Context-Sensitive Correlation of Implicitly Related Data: An Episode Creation Methodology
Episode creation is the task of classifying medical events and related clinical data to high-level concepts, such as diseases. Challenges in episode creation result in part because...
Roderick Y. Son, Ricky K. Taira, Hooshang Kangarlo...
ATAL
2010
Springer
13 years 9 months ago
Robust Bayesian methods for Stackelberg security games
Recent work has applied game-theoretic models to real-world security problems at the Los Angeles International Airport (LAX) and Federal Air Marshals Service (FAMS). The analysis o...
Christopher Kiekintveld, Milind Tambe, Janusz Mare...
PVLDB
2008
146views more  PVLDB 2008»
13 years 7 months ago
Efficient search for the top-k probable nearest neighbors in uncertain databases
Uncertainty pervades many domains in our lives. Current real-life applications, e.g., location tracking using GPS devices or cell phones, multimedia feature extraction, and sensor...
George Beskales, Mohamed A. Soliman, Ihab F. Ilyas
WSC
1998
13 years 9 months ago
Rostering by Iterating Integer Programming and Simulation
We present a new technique (RIIPS) for solving rostering problems in the presence of service uncertainty. RIIPS stands for "Rostering by Iterating Integer Programming and Sim...
Shane G. Henderson, Andrew J. Mason