Machine Learning
Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast condensed nearest neighbor rule
ICML '05 Proceedings of the 22nd international conference on Machine learning
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
Supervised pattern classification based on optimum-path forest
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Fast and accurate holistic face recognition using optimum-path forest
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Optimizing Optimum-Path Forest Classification for Huge Datasets
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
A Comparison between Optimum-Path Forest and k-Nearest Neighbors Classifiers
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
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This paper presents the k-Optimum Path Forest (k-OPF) supervised classifier, which is a natural extension of the OPF classifier. k-OPF is compared to the k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers, and we see that k-OPF and k-NN have many similarities. This work shows that the k-OPF is equivalent to the k-NN classifier when all training samples are used as prototypes. Simulations comparing the accuracy results, the decision boundaries and the processing time of the classifiers are presented to experimentally validate our hypothesis. Also, we prove that OPF using the max cost function and the NN supervised classifiers have the same theoretical error bounds.