Lazy learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organizing Maps
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
Building Localized Basis Function Networks Using Context Dependent Clustering
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Space-time tradeoffs for approximate nearest neighbor searching
Journal of the ACM (JACM)
Geometric decision rules for instance-based learning problems
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Selection of prototype rules: context searching via clustering
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Simplifying SVM with weighted LVQ algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ highly depends on proper initialization of prototypes and the optimization mechanism. Prototype initialization based on context dependent clustering is introduced, and modification of the LVQ cost function that utilizes additional information about class-dependent distribution of training vectors. The new method is illustrated on 6 benchmark datasets, finding simple and accurate models of data in form of prototype-based rules.