Machine Learning
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
Advances in kernel methods
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A decision based one-against-one method for multi-class support vector machine
Pattern Analysis & Applications
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Nesting Algorithm for Multi-Classification Problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Data-driven decomposition for multi-class classification
Pattern Recognition
Multi-Space-Mapped SVMs for Multi-class Classification
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
An overview of statistical learning theory
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
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The binary tree support vector machine (SVM) algorithm is one of the mainstream algorithms for multi-class classification in the fields of pattern recognition and machine learning. In order to reduce the training and testing time of one-against-all SVM (OAA-SVM) and reduced OAA-SVM (R-OAA-SVM), in this study, two OAA partition based binary tree SVM algorithms are proposed for multi-class classification. One is the single-space-mapped binary tree SVM (SBT-SVM) and the other is the multi-space-mapped binary tree SVM (MBT-SVM). In the proposed two algorithms, the best OAA partition is determined for each non-leaf node and the k-fold cross validation strategy is adopted to obtain the optimal classifiers. A set of experiments is conducted on nine UCI datasets and two face recognition datasets to demonstrate their performances. The results show that in term of testing accuracy, MBT-SVM is comparable with one-against-one SVM (OAO-SVM), R-OAA-SVM and OAA-SVM and superior to SBT-SVM. In term of testing time, MBT-SVM is superior to OAO-SVM, binary tree of SVM (BTS), R-OAA-SVM and OAA-SVM and slightly longer than SBT-SVM. In term of training time, MBT-SVM is superior to BTS, R-OAA-SVM and OAA-SVM and comparable with SBT-SVM. For the datasets with smaller class number and training sample number, the training time of MBT-SVM is comparable with that of OAO-SVM. For the datasets with larger class number or training sample number, in most cases, the training time of MBT-SVM is longer than that of OAO-SVM.