A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Adaptive Quasiconformal Kernel Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Adaptive relevance matrices in learning vector quantization
Neural Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
LDA/SVM driven nearest neighbor classification
IEEE Transactions on Neural Networks
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Introducing adaptive metric has been shown to improve the results of distance-based classification algorithms. Existing methods are often computationally intensive, either in the training or in the classification phase. We present a novel algorithm that we call Cluster-Based Adaptive Metric (CLAM) classification. It first determines the number of clusters in each class of a training set and then computes the parameters of a Mahalanobis distance for each cluster. The derived Mahalanobis distances are then used to estimate the probability of cluster- and, subsequently, class-membership. We compare the proposed algorithm with other classification algorithms using 10 different data sets. The proposed CLAM algorithm is as effective as other adaptive metric classification algorithms yet it is simpler to use and in many cases computationally more efficient.