C4.5: programs for machine learning
C4.5: programs for machine learning
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
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This article sets out to demonstrate how boosting can serve as a supervised classification method, and to compare its results with those of conventional methods. The comparison begins with a theoretical example in which several criteria are varied: number of pixels per class, overlapping (or not) of radiometric values between classes, and presence and absence of spatial structuring of classes within the geographical space. The results are then compared with a real case study of land cover based on a multispectral SPOT image of the Sousson catchment area (South of France). It is seen that (1) maximum likelihood gives better results than boosting when the radiometric values for each class are clearly separated. This advantage is lost as the number of pixels per class increases; (2) boosting is systematically better than maximum likelihood in the event of overlapping radiometric variable classes, whether or not there is a spatial structure.