Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Data-dependent structural risk minimisation for perceptron decision trees
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Enlarging the Margins in Perceptron Decision Trees
Machine Learning
Machine Learning
Scaling multi-class support vector machines using inter-class confusion
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Hierarchical Classifier Using New Support Vector Machine
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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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In this paper we review and evaluate recent decision tree approaches to multi-class SVM for benchmark and self-collected image data sets. In addition, we compare the classification capabilities of hierarchical agglomerative and hierarchical divisive clustering approaches which recursively partition the set of classes with the standard pair wise classifier. We compare agglomerative clustering approaches based on the pair wise Euclidean distance of class means, pair wise misclassification rates for a binary SVM and a Mahalanobis-assignment as well as divisive clustering using k-Means to partition a set of classes based on a partition of the data or one-class-ν-SVM class representatives. Our results show that decision tree approaches achieve classification performance similar to the default multi-class SVM.