A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Application of the cross entropy method to the GLVQ algorithm
Pattern Recognition
Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network
Expert Systems with Applications: An International Journal
Learning vector quantization with adaptive prototype addition and removal
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
Improving the GRLVQ algorithm by the cross entropy method
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Weighted learning vector quantization to cost-sensitive learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A fast VQ codebook search with initialization and search order
Information Sciences: an International Journal
Influence of class distribution on cost-sensitive learning: A case study of bankruptcy analysis
Intelligent Data Analysis
| Hi-index | 0.01 |
A learning vector quantization (LVQ) algorithm called harmonic to minimum LVQ algorithm (H2M-LVQ) is presented to tackle the initialization sensitiveness problem associated with the original generalized LVQ (GLVQ) algorithm. Experimental results show superior performance of the H2M-LVQ algorithm over the GLVQ and one of its variants on several datasets. datasets.