Machine Learning
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
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 Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Adaptive simplification of solution for support vector machine
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
ACE-Cost: acquisition cost efficient classifier by hybrid decision tree with local SVM leaves
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Hybrid decision tree architecture utilizing local SVMs for multi-label classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Hierarchical linear support vector machine
Pattern Recognition
Decision trees: a recent overview
Artificial Intelligence Review
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
A hybrid decision tree classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We have proposed a hybrid SVM based decision tree to speedup SVMs in its testing phase for binary classification tasks. While most existing methods addressed towards this task aim at reducing the number of support vectors, we have focused on reducing the number of test datapoints that need SVM's help in getting classified. The central idea is to approximate the decision boundary of SVM using decision trees. The resulting tree is a hybrid tree in the sense that it has both univariate and multivariate (SVM) nodes. The hybrid tree takes SVM's help only in classifying crucial datapoints lying near decision boundary; remaining less crucial datapoints are classified by fast univariate nodes. The classification accuracy of the hybrid tree is guaranteed by tuning a threshold parameter. Extensive computational comparisons on 19 publicly available datasets indicate that the proposed method achieves significant speedup when compared to SVMs, without any compromise in classification accuracy.