1994 Special Issue: Design and evolution of modular neural network architectures
Neural Networks - Special issue: models of neurodynamics and behavior
Machine Learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines
Pattern Analysis & Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient method for simplifying support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Comparison between error correcting output codes and fuzzy support vector machines
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Data-driven decomposition for multi-class classification
Pattern Recognition
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation
Image and Vision Computing
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Separability of ternary codes for sparse designs of error-correcting output codes
Pattern Recognition Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Multicategory classification based on the hypercube self-organizing mapping (SOM) scheme
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
Radar HRRP target recognition based on higher order spectra
IEEE Transactions on Signal Processing
Structurally adaptive modular networks for nonstationary environments
IEEE Transactions on Neural Networks
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
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Aiming at decomposing a complex multi-class problem into fewer and simpler sub-problems to gain an overall classifier of low complexity, we propose a universal data-driven topology-preserving output code (TPOC) scheme, and a computationally efficient supervised circular learning algorithm (CLA) for the learning of the required TPOC map in the scheme. The scheme leads to a compact code and low complexity, and is an extension of binary, ternary and ECOC code. Experiments on Iris data, NCI data, octaphase-shift-keying data and handwritten digits reveal that the scheme substantially outperforms DECOC, one-against-all, natural coding and ECOC in using a less complex classifier with no loss or even enhanced generalization performance: the total number of support vectors is reduced greatly in SVM study and that of synaptic weights is greatly reduced (e.g., by 86% with training time reduced by 98% in MLP study in handwritten digit recognition problem); the total number of synaptic weights is further reduced by about one-fourth with less than one-hundredth loss of generalization performance when classifier complexities are assigned adaptive to the coding process. Finally, it is successfully applied to automatic target recognition based on a real measured radar data.