Performance analysis of LVQ algorithms: a statistical physics approach
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Margin-based active learning for LVQ networks
Neurocomputing
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
Prototype based fuzzy classification in clinical proteomics
International Journal of Approximate Reasoning
Vector Quantization of Images Using a Fuzzy Clustering Method
Cybernetics and Systems
A sparse gaussian processes classification framework for fast tag suggestions
Proceedings of the 17th ACM conference on Information and knowledge management
Nearest prototype classification of noisy data
Artificial Intelligence Review
Adaptive relevance matrices in learning vector quantization
Neural Computation
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
Color image vector quantization using an enhanced self-organizing neural network
CIS'04 Proceedings of the First international conference on Computational and Information Science
Prototype based classification using information theoretic learning
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Medical image vector quantizer using wavelet transform and enhanced SOM algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A study of the robustness of KNN classifiers trained using soft labels
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Local metric adaptation for soft nearest prototype classification to classify proteomic data
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Decentralized Estimation using distortion sensitive learning vector quantization
Pattern Recognition Letters
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
| Hi-index | 0.00 |
We propose a new method for the construction of nearest prototype classifiers which is based on a Gaussian mixture ansatz and which can be interpreted as an annealed version of learning vector quantization (LVQ). The algorithm performs a gradient descent on a cost-function minimizing the classification error on the training set. We investigate the properties of the algorithm and assess its performance for several toy data sets and for an optical letter classification task. Results show 1) that annealing in the dispersion parameter of the Gaussian kernels improves classification accuracy; 2) that classification results are better than those obtained with standard learning vector quantization (LVQ 2.1, LVQ 3) for equal numbers of prototypes; and 3) that annealing of the width parameter improved the classification capability. Additionally, the principled approach provides an explanation of a number of features of the (heuristic) LVQ methods.