International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Inferring decision trees using the minimum description length principle
Information and Computation
Learning decision trees from random examples needed for learning
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Reducing the run-time complexity in support vector machines
Advances in kernel methods
Generalization in decision trees and DNF: does size matter?
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Enlarging the Margins in Perceptron Decision Trees
Machine Learning
A parallel mixture of SVMs for very large scale problems
Neural Computation
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Generalization Error Bounds for Threshold Decision Lists
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Cost-sensitive learning and decision making for massachusetts pip claim fraud data
International Journal of Intelligent Systems
Learning with Decision Lists of Data-Dependent Features
The Journal of Machine Learning Research
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast Support Vector Machine Classification using linear SVMs
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Sample compression bounds for decision trees
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
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
SVM optimization: inverse dependence on training set size
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
The Journal of Machine Learning Research
A Streaming Parallel Decision Tree Algorithm
The Journal of Machine Learning Research
A hybrid SVM based decision tree
Pattern Recognition
What should be minimized in a decision tree?
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Mixing linear SVMs for nonlinear classification
IEEE Transactions on Neural Networks
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome
Expert Systems with Applications: An International Journal
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The increasing size and dimensionality of real-world datasets make it necessary to design efficient algorithms not only in the training process but also in the prediction phase. In applications such as credit card fraud detection, the classifier needs to predict an event in 10ms at most. In these environments the speed of the prediction constraints heavily outweighs the training costs. We propose a new classification method, called a Hierarchical Linear Support Vector Machine (H-LSVM), based on the construction of an oblique decision tree in which the node split is obtained as a Linear Support Vector Machine. Although other methods have been proposed to break the data space down in subregions to speed up Support Vector Machines, the H-LSVM algorithm represents a very simple and efficient model in training but mainly in prediction for large-scale datasets. Only a few hyperplanes need to be evaluated in the prediction step, no kernel computation is required and the tree structure makes parallelization possible. In experiments with medium and large datasets, the H-LSVM reduces the prediction cost considerably while achieving classification results closer to the non-linear SVM than that of the linear case.