Introduction to artificial neural systems
Introduction to artificial neural systems
Modeling of dynamic systems
The nature of statistical learning theory
The nature of statistical learning theory
Self-organizing maps
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Machine Learning
Scientific Computing: An Introductory Survey
Scientific Computing: An Introductory Survey
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Problems of learning on manifolds
Problems of learning on manifolds
Advanced Computer Architecture and Parallel Processing (Wiley Series on Parallel and Distributed Computing)
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
Comparison of SOM Point Densities Based on Different Criteria
Neural Computation
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A novel and quick SVM-based multi-class classifier
Pattern Recognition
A survey of kernel and spectral methods for clustering
Pattern Recognition
A New Orientation for Multi-Class SVM
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning
A SVM-based discretization method with application to associative classification
Expert Systems with Applications: An International Journal
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
International Journal of Approximate Reasoning
A penalized likelihood based pattern classification algorithm
Pattern Recognition
A Tree-Based Multi-class SVM Classifier for Digital Library Document
MMIT '08 Proceedings of the 2008 International Conference on MultiMedia and Information Technology
Tensor-based transductive learning for multimodality video semantic concept detection
IEEE Transactions on Multimedia
A new RBF neural network with boundary value constraints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
R-POPTVR: a novel reinforcement-based POPTVR fuzzy neural network for pattern classification
IEEE Transactions on Neural Networks
Multiple-view multiple-learner active learning
Pattern Recognition
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Semantic label sharing for learning with many categories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
Personalized mode transductive spanning SVM classification tree
Information Sciences: an International Journal
Self-organising maps in document classification: a comparison with six machine learning methods
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Active Learning Based on Locally Linear Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale cancer modeling: In the line of fast simulation and chemotherapy
Mathematical and Computer Modelling: An International Journal
Adaptive-Fourier-Neural-Network-Based Control for a Class of Uncertain Nonlinear Systems
IEEE Transactions on Neural Networks
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Mapping to Multidimensional Optimal Regions M^{2}OR is the enhanced version of Mapping to Optimal Regions MOR which is a special purposed method for multiclass classification task. Similar to MOR, it reduces computational complexity; however, presents better accuracy. Theoretical and experimental results confirm that by using M^{2}OR, the minimum computational complexity of a multi-classification task is approximately equal to one inner product in feature space. As a multi-classifier, MOR family generalizes the upper bound of Vapnik-Chervonenkis V.C. entropy and growth function. Corresponding properties are updated proportionally for real functions. It is shown that V.C. dimension of MOR family is controllable using parameters of the model. With respect to the theorem of Solution Existence, MOR family is able to classify every partitionable feature space.