Instance-Based Learning Algorithms
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Cooperative Game Theoretic Approach to Prototype Selection
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A review of instance selection methods
Artificial Intelligence Review
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The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimization, so they have good generalization ability. We proposed a new prototype selection method based on support vectors for nearest neighbor rules. It selects prototypes only from support vectors. During classification, for unknown example, it can be classified into the same class as the nearest neighbor in feature space among all the prototypes. Computational results show that our method can obtain higher reduction rate and accuracy than popular condensing or editing instance reduction method.