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
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
Links between perceptrons, MLPs and SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Concept boundary detection for speeding up SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multiclass reduced-set support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
A discrete approach for supervised pattern recognition
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Novel Approaches for Exclusive and Continuous Fingerprint Classification
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A Learning Algorithm for the Optimum-Path Forest Classifier
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
Computers and Electrical Engineering
An Optimum-Path Forest framework for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
We have shown a supervised approach for pattern classification, which interprets the training samples as nodes of a complete arc-weighted graph and computes an optimum-path forest rooted at some of the closest samples between distinct classes. A new sample is classified by the label of the root which offers to it the optimum path. We propose a variant, in which the training samples are the nodes of a graph, whose the arcs are the k -nearest neighbors in the feature space. The graph is weighted on the nodes by their probability density values (pdf) and the optimum-path forest is rooted at the maxima of the pdf. The best value of k is computed by the maximum accuracy of classification in the training set. A test sample is assigned to the class of the maximum, which offers to it the optimum path. Preliminary results have shown that the proposed approach can outperform the previous one and the SVM classifier in some datasets.