Pattern Selection for Support Vector Classifiers
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Invariance of neighborhood relation under input space to feature space mapping
Pattern Recognition Letters
Neighborhood Property--Based Pattern Selection for Support Vector Machines
Neural Computation
Reducing examples to accelerate support vector regression
Pattern Recognition Letters
An algorithm to cluster data for efficient classification of support vector machines
Expert Systems with Applications: An International Journal
Data Selection Using SASH Trees for Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Selecting Samples and Features for SVM Based on Neighborhood Model
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Block-quantized support vector ordinal regression
IEEE Transactions on Neural Networks
Fast pattern selection for support vector classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Bootstrap based pattern selection for support vector regression
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Training data selection for support vector machines
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Fast training of SVM via morphological clustering for color image segmentation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Pattern selection for support vector regression based response modeling
Expert Systems with Applications: An International Journal
Fast instance selection for speeding up support vector machines
Knowledge-Based Systems
Neighbors' distribution property and sample reduction for support vector machines
Applied Soft Computing
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A procedure called SVM-KM, based on clustering by k-means and to accelerate the training of Support Vector Machine, is the main objective of the present work. During the Support Vector Machines (SVMs) optimization phase, training vectors near the separation margins are likely to become support vector and must be preserved. Conversely, training vectors far from the margins are not in general taken into account for SVM's design process. SVM-KM groups the training vector in many clusters. Clusters formed only by a vector that belongs to the same class label can be disregard and only cluster centers are used. On the other hand, clusters with more than one class label are unchanged and all training vectors belonging to them are considered. Cluster with mixed composition are likely to happen near the separation margins and they may hold some support vectors. Consequently, the number of vectors in a SVM training is smaller and the training time can be decreased without compromising the generalization capability of the SVM.