Ortega J, Koppel M, Argamon S. (2001) Arbitrating Among Competing Classifiers Using Learned Referees. Knowledge and Information Systems 3, 470-490 Home/Search Document Details and Download
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Transductive Reliability Estimation for Medical Diagnosis - Kukar (2002) (Correct)
.... is some ongoing work for constructing classifiers that divide the data 247 space into regions that are reliable and regions that are not reliable [1] Such meta 248 learning approaches have also been used for picking the most reliable prediction 249 from the outputs of an ensemble of classifiers [23, 28]. 250 Several methods for inducing probabilistic descriptions from training data, fig 251 uring the use of density estimation algorithms, have emerged as an alternative to 252 more established approaches for Machine Learning. Frequently kernel density es 253 timation [29] is used for density ....
J. Ortega, M. Koppel, and S. Argamon. Arbitrating among competing classifiers 826 using learned referees. Knowledge and Information Systems Journal, 3:470--490, 827 2001. 828 39
Distributed Data Mining Systems - Prodromidis (1999) (Correct)
....Perrone Cooper, 1993; Schapire, 1990; Tresp Taniguchi, 1995 ] techniques that employ referee functions to arbitrate among the predictions generated by the classifiers [ Chan Stolfo, 1993b; Jacobs et al. 1991; Jordan Xu, 1993; R. J. 1994; Jordan Jacobs, 1994; Kong Dietterich, 1995; Ortega, Koppel, Argamon Engelson, 1999 ] methods that rely on principal components analysis [ Merz, 1999; Merz Pazzani, 1999 ] or methods that apply inductive learning techniques to learn the behavior and properties of the candidate classifiers [ Wolpert, 1992; Chan Stolfo, 1993b ] In this thesis, we describe a distributed ....
.... J. 1994; Jordan Jacobs, 1994 ] for example, the input space is divided into a series of overlapping regions using probabilistic methods, while special gating functions are trained to choose between experts (neural network classifiers) over these regions. A similar approach is described in [ Ortega, Koppel, ArgamonEngelson, 1999 ] where referee predictors, in the form of decision trees, provide confidence estimates on the expertise of each base classifier on di#erent sub domains. Arbitrating Arbitration, entails the use of an objective judge whose own prediction is selected if the participating classifiers cannot ....
Ortega, J.; Koppel, M.; and ArgamonEngelson, S. 1999. Arbitrating among competing classifiers using learned referees. Machine Learning. in press.
Cost Complexity Pruning of Ensemble Classifiers - Prodromidis, Stolfo (Correct)
....sites. Several methods for integrating ensembles of models have been studied, including techniques that combine the set of models in some linear fashion [1, 2, 3, 12, 20, 27, 29, 37, 39, 21] techniques that employ referee functions to arbitrate among the predictions generated by the classifiers, [16, 17, 18, 19, 28, 36], methods that rely on principal components analysis [23, 24] or methods that apply inductive learning techniques to learn the behavior and properties of the candidate classifiers [6, 40] Constructing ensembles of classifiers is not cheap and produces a final outcome that is expensive due to the ....
J. Ortega, M. Koppel, and S. Argamon-Engelson. Arbitrating among competing classifiers using learned referees. Machine Learning, 1999. in press.
Cost Complexity-based Pruning of Ensemble Classifiers - Prodromidis, Stolfo (1999) (6 citations) (Correct)
....at di#erent sites. Several methods for integrating ensembles of models have been studied, including techniques that combine the set of models in some linear fashion (egs. 10, 2, 1] techniques that employ referee functions to arbitrate among the predictions generated by the classifiers, [13, 14, 19], methods that rely on principal components analysis [16, 17] or methods that apply inductive learning techniques to learn the behavior and properties of the candidate classifiers [26, 5] Constructing ensembles of classifiers is not cheap and produces a final outcome that is expensive due to the ....
J. Ortega, M. Koppel, and S. Argamon-Engelson. Arbitrating among competing classifiers using learned referees. Machine Learning, 1998. to appear.
Competent Undemocratic Committees - Duch, Itert, Grudzinski (2002) (1 citation) (Correct)
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Ortega J, Koppel M, Argamon S. (2001) Arbitrating Among Competing Classifiers Using Learned Referees. Knowledge and Information Systems 3, 470-490
Delegating Classifiers - Esar Ferri Cferri (2004) (2 citations) (Correct)
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Ortega, J., Koppel, M., &Argamon, S. (2001). Arbitrating among competing classifiers using learned referees. Knowledge and Information Systems, 3, 470--490.
Transductive Reliability Estimation for Medical Diagnosis - Kukar (2002) (Correct)
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J. Ortega, M. Koppel, and S. Argamon. Arbitrating among competing classifiers using learned referees. Knowledge and Information Systems Journal, 3:470--490, 2001.
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