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NIPS
2008
15 years 6 months ago
Generative and Discriminative Learning with Unknown Labeling Bias
We apply robust Bayesian decision theory to improve both generative and discriminative learners under bias in class proportions in labeled training data, when the true class propo...
Miroslav Dudík, Steven J. Phillips
AAAI
1997
15 years 6 months ago
The Sounds of Silence: Towards Automated Evaluation of Student Learning in a Reading Tutor that Listens
1 We propose a paradigm for ecologically valid, authentic, unobtrusive, automatic, data-rich, fast, robust, and sensitive evaluation of computer-assisted student performance. We i...
Jack Mostow, Gregory Aist
JMLR
2008
104views more  JMLR 2008»
15 years 4 months ago
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...
Giorgio Corani, Marco Zaffalon
PAMI
2007
137views more  PAMI 2007»
15 years 4 months ago
Biometrics from Brain Electrical Activity: A Machine Learning Approach
—The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framewor...
Ramaswamy Palaniappan, Danilo P. Mandic
ICML
2006
IEEE
16 years 5 months ago
Kernelizing the output of tree-based methods
We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...