Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
Ordinal Regression with K-SVCR Machines
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A new approach to qualitative learning in time series
Expert Systems with Applications: An International Journal
Comparison of Neural Classification Algorithms Applied to Land Cover Mapping
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Multi-classification with tri-class support vector machines: a review
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Fuzzy-input fuzzy-output one-against-all support vector machines
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Support vector machines for classification of input vectors with different metrics
Computers & Mathematics with Applications
Learning from label preferences
DS'11 Proceedings of the 14th international conference on Discovery science
A study on output normalization in multiclass SVMs
Pattern Recognition Letters
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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The standard form for dealing with multi-class classification problems when bi-classifiers are used is to consider a two-phase (decomposition, reconstruction) training scheme. The most popular decomposition procedures are pairwise coupling (one versus one, 1-v-1), which considers a learning machine for each Pair of classes, and the one-versus-all scheme (one versus all, 1-v-r), which takes into consideration each class versus the remaining classes. In this article a 1-v-1 tri-class Support Vector Machine (SVM) is presented. The expansion of the architecture of this machine into three categories specifically addresses the decomposition problem of how to prevent the loss of information which occurs in the usual 1-v-1 training procedure. The proposed machine, by means of a third class, allows all the information to be incorporated into the remaining training patterns when a multi-class problem is considered in the form of a 1-v-1 decomposition. Three general structures are presented where each improves some features from the precedent structure. In order to deal with multi-classification problems, it is demonstrated that the final machine proposed allows ordinal regression as a form of decomposition procedure. Examples and experimental results are presented which illustrate the performance of the new tri-class SV machine.