Introduction to algorithms
Optimal Correspondence of String Subsequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A compact and efficient image retrieval approach based on border/interior pixel classification
Proceedings of the eleventh international conference on Information and knowledge management
BAS: a perceptual shape descriptor based on the beam angle statistics
Pattern Recognition Letters
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Object delineation by κ-connected components
EURASIP Journal on Advances in Signal Processing
A discrete approach for supervised pattern recognition
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
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The Optimum-Path Forest (OPF) classifier is a novel graph-based supervised pattern recognition technique that has been demonstrated to be superior to Artificial Neural Networks and similar to Support Vector Machines, but much faster. The OPF classifier reduces the problem of pattern recognition to a computation of an optimum-path forest in the feature space induced by a graph, creating discrete optimal partitions, which are optimum-path trees rooted by prototypes, i.e., key samples that will compete among themselves trying to conquer the remaining samples. Some applications, such that medical specialist systems for image-based diseases identification, need to be constantly re-trained with new instances (diagnostics) to achieve a better generalization of the problem, which requires large storage devices, due to the high number of generated data (millions of voxels). In that way, we present here a pruning algorithm for the OPF classifier that learns the most irrelevant samples and eliminate them from the training set, without compromising the classifier's accuracy.