Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
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
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Protein cellular localization prediction with Support Vector Machines and Decision Trees
Computers in Biology and Medicine
Developing Domain-Specific Gesture Recognizers for Smart Diagram Environments
Graphics Recognition. Recent Advances and New Opportunities
Supplier selection based on hierarchical potential support vector machine
Expert Systems with Applications: An International Journal
One-against-all-based multiclass SVM strategies applied to vehicle plate character recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A hierarchical classifier applied to multi-way sentiment detection
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Protein cellular localization with multiclass support vector machines and decision trees
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
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Support Vector Machines constitute a powerful Machine Learning technique originally designed for the solution of 2-class problems. In multiclass applications, many works divide the whole problem in multiple binary subtasks, whose results are then combined. This paper introduces a new framework for multiclass Support Vector Machines generation from binary predictors. Minimum Spanning Trees are used in the obtainment of a hierarchy of binary classifiers composing the multiclass solution. Different criteria were tested in the tree design and the results obtained evidence the efficiency of the proposed approach, which is able to produce good hierarchical multiclass solutions in polynomial time.