The Strength of Weak Learnability
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
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
Towards scalable support vector machines using squashing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Enlarging the Margins in Perceptron Decision Trees
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Scaling Kernel-Based Systems to Large Data Sets
Data Mining and Knowledge Discovery
Machine Learning
A parallel mixture of SVMs for very large scale problems
Neural Computation
Large Margin Trees for Induction and Transduction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Margin Distribution Bounds on Generalization
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing
Data Mining and Knowledge Discovery
Neural Computation
Concept boundary detection for speeding up SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
Margin Trees for High-dimensional Classification
The Journal of Machine Learning Research
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A Novel SVM Decision Tree and its application to Face Detection
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Optimized cutting plane algorithm for support vector machines
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
The Journal of Machine Learning Research
IEEE Transactions on Signal Processing
On the generalization of soft margin algorithms
IEEE Transactions on Information Theory
Large-scale Gaussian process classification using random decision forests
Pattern Recognition and Image Analysis
Hierarchical linear support vector machine
Pattern Recognition
Universal consistency of localized versions of regularized kernel methods
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
Expert Systems with Applications: An International Journal
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To handle problems created by large data sets, we propose a method that uses a decision tree to decompose a given data space and train SVMs on the decomposed regions. Although there are other means of decomposing a data space, we show that the decision tree has several merits for large-scale SVM training. First, it can classify some data points by its own means, thereby reducing the cost of SVM training for the remaining data points. Second, it is efficient in determining the parameter values that maximize the validation accuracy, which helps maintain good test accuracy. Third, the tree decomposition method can derive a generalization error bound for the classifier. For data sets whose size can be handled by current non-linear, or kernel-based, SVM training techniques, the proposed method can speed up the training by a factor of thousands, and still achieve comparable test accuracy.