A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting Neighborhood-Based Classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
On the Consistency of Information Filters for Lazy Learning Algorithms
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Stopping criterion for boosting based data reduction techniques: from binary to multiclass problem
The Journal of Machine Learning Research
Fast condensed nearest neighbor rule
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
Distributed Nearest Neighbor-Based Condensation of Very Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Avoiding Boosting Overfitting by Removing Confusing Samples
ECML '07 Proceedings of the 18th European conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Regularization on discrete spaces
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Large margin nearest neighbor classifiers
IEEE Transactions on Neural Networks
A class boundary preserving algorithm for data condensation
Pattern Recognition
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
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Graph theory has been shown to provide a powerful tool for representing and tackling machine learning problems, such as clustering, semi-supervised learning, and feature ranking. This paper proposes a graph-based discrete differential operator for detecting and eliminating competence-critical instances and class label noise from a training set in order to improve classification performance. Results of extensive experiments on artificial and real-life classification problems substantiate the effectiveness of the proposed approach.