The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Reducing the run-time complexity in support vector machines
Advances in kernel methods
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact simplification of support vector solutions
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Improving Multiclass Pattern Recognition by the Combination of Two Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass reduced-set support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Adaptive simplification of solution for support vector machine
Pattern Recognition
Simplifying mixture models through function approximation
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
Multiclass SVM classification using graphs calibrated by similarity between classes
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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We present an improved version of One-Against-All (OAA) method for multiclass SVM classification based on a decision tree approach. The proposed decision tree based OAA (DT-OAA) is aimed at increasing the classification speed of OAA by using posterior probability estimates of binary SVM outputs. DT-OAA decreases the average number of binary SVM tests required in testing phase to a greater extent when compared to OAA and other multiclass SVM methods. For a balanced multiclass dataset with K classes, under best situation, DT-OAA requires only (K + 1)/2 binary tests on an average as opposed to K binary tests in OAA; however, on imbalanced multiclass datasets we observed DT-OAA to be much faster with proper selection of order in which the binary SVMs are arranged in the decision tree. Computational comparisons on publicly available datasets indicate that the proposed method can achieve almost the same classification accuracy as that of OAA, but is much faster in decision making.