Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Specialization of perceptual processes
Specialization of perceptual processes
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Artificial Intelligence in Medicine
An efficient color representation for image retrieval
IEEE Transactions on Image Processing
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Combining the predictions of a set of classifiers has shown to be an effective way of creating composite classifiers that are more accurate than any of the component classifiers; we have performed a research work consisting of the design, development and experimental use of a multi-classifier system for image analysis and surface classification of the different segments that might appear on a given picture in order to help a Mobile Robot in its navigation task. The presented approach combines a number of component classifiers which are standard machine learning classification algorithms, using a second layer paradigm to obtain a better classification accuracy. Experimental results have been obtained using a datafile of cases that contains information about surfaces, extracted from images obtained by the robot. The classification problem consists of recognizing to which of the surfaces belongs a n × n size subimage. The accuracy obtained using the presented new approach statistically improves those obtained using standard machine learning methods.