Searching for authors named "Patrice Latinne" – sorted by Relevance.
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Any Reasonable Cost Function Can Be Used for a Posteriori Probability Approximation
- In this letter, we provide a straightforward proof of an important, but nevertheless little known, result obtained by Lindley in the context of subjective probability theory. This result, once interpreted in the machine learning/pattern recognition context, puts new lights on the probabilistic i
- Cited by 2 (1 self) – Add To MetaCart
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Limiting the Number of Trees in Random Forests
- Abstract. The aim of this paper is to propose a simple procedure that aprioridetermines a minimum number of classifiers to combine in order to obtain a prediction accuracy level similar to the one obtained with the combination of larger ensembles. The procedure is based on the McNemar non-parametric
- Cited by 1 (0 self) – Add To MetaCart
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Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a Multi-Class Problem in Remote Sensing
- In the present study, we introduce a simple iterative procedure that allows to correct the outputs of a classifier with respect to the new a priori probabilities of a new data set to be scored, even when these new a priori probabilities are unknown in advance. We also show that a significant i
- Cited by 11 (1 self) – Add To MetaCart
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Remote Sensing Classification Of Spectral, Spatial And Contextual Data Using Multiple Classifier Systems
- This study promotes the use of a multiclassifier system (MCS) fed with high-resolution remote sensing data coupled with contextual and textural data in the domain of land cover and land use classification. The gain of this approach is shown by a favorable comparison of our BAGFS classifier (a mix
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Pattern Analysis Applications (2002)5:201--209
- Several ways of manipulating a training set have shown that weakened classifier combination can improve prediction accuracy.
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Adjusting the Outputs of a Classifier to New a
- It sometimes happens, for instance in case-control studies, that a classifier is trained on a data set which does not reflect the true a priori probabilities of the target classes on real-world data. This may have a negative e#ect on the classification accuracy obtained on the real-world data se
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