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» Making generative classifiers robust to selection bias
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ML
2000
ACM
14 years 9 months ago
Maximizing Theory Accuracy Through Selective Reinterpretation
Existing methods for exploiting awed domain theories depend on the use of a su ciently large set of training examples for diagnosing and repairing aws in the theory. In this paper,...
Shlomo Argamon-Engelson, Moshe Koppel, Hillel Walt...
FLAIRS
1998
14 years 11 months ago
Investigating the Validity of a Test Case Selection Methodology for Expert System Validation
Providing assurances of performance is an important aspect of successful development and commercialization of expert systems. However, this can only be done if the quality of the ...
Jan-Eike Michels, Thomas Abel, Rainer Knauf, Aveli...
CP
2011
Springer
13 years 9 months ago
Algorithm Selection and Scheduling
Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifyin...
Serdar Kadioglu, Yuri Malitsky, Ashish Sabharwal, ...
AINA
2007
IEEE
15 years 4 months ago
Detecting Coordinated Distributed Multiple Attacks
This paper describes results concerning the robustness and generalization capabilities of kernel methods in detecting coordinated distributed multiple attacks (CDMA) using network...
Srinivas Mukkamala, Krishna Yendrapalli, Ram B. Ba...
ICASSP
2011
IEEE
14 years 1 months ago
Phoneme selective speech enhancement using the generalized parametric spectral subtraction estimator
In this study, the generalized parametric spectral subtraction estimator is employed in the context of a ROVER speech enhancement framework to develop a robust phoneme class selec...
Amit Das, John H. L. Hansen