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» Classifier Combining Rules Under Independence Assumptions
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COLING
1996
13 years 7 months ago
Interpretation of Nominal Compounds: Combining Domain-Independent and Domain-Specific Information
A domain independent model is proposed for the automated interpretation of nominal compounds in English. This model is meant to account for productive rules of interpretation whic...
Cécile Fabre
ML
2000
ACM
154views Machine Learning» more  ML 2000»
13 years 6 months ago
Lazy Learning of Bayesian Rules
The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
Zijian Zheng, Geoffrey I. Webb
ICASSP
2008
IEEE
14 years 21 days ago
Bringing diverse classifiers to common grounds: dtransform
Several classification scenarios employ multiple independently trained classifiers and the outputs of these classifiers need to be combined. However, since each of the trained ...
Devi Parikh, Tsuhan Chen
ICPR
2000
IEEE
13 years 10 months ago
A Theoretical Framework for Dynamic Classifier Selection
At present, the common operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers pointed out the potentialities of ...
Giorgio Giacinto, Fabio Roli
CDC
2010
IEEE
164views Control Systems» more  CDC 2010»
13 years 1 months ago
Accuracy and decision time for a class of sequential decision aggregation rules
Abstract-- This work focuses on decentralized decision making in a population of individuals each implementing the sequential probability ratio test. The individual decisions are c...
Sandra H. Dandach, Ruggero Carli, Francesco Bullo