The nature of statistical learning theory
The nature of statistical learning theory
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
A New Convex Hull Algorithm for Planar Sets
ACM Transactions on Mathematical Software (TOMS)
Convex hulls of finite sets of points in two and three dimensions
Communications of the ACM
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Neural Computation
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
BORDER: Efficient Computation of Boundary Points
IEEE Transactions on Knowledge and Data Engineering
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
Reducing examples to accelerate support vector regression
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Selecting training points for one-class support vector machines
Pattern Recognition Letters
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
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The training of a support vector machine (SVM) has the time complexity of O(n^3) with data number n. Normal SVM algorithms are not suitable for classification of large data sets. Convex hull can simplify SVM training, however the classification accuracy becomes lower when there are inseparable points. This paper introduces a novel method for SVM classification, called convex-concave hull. After grid pre-processing, the convex hull and the concave (non-convex) hull are found by Jarvis march method. Then the vertices of the convex-concave hull are applied for SVM training. The proposed convex-concave hull SVM classifier has distinctive advantages on dealing with large data sets with higher accuracy. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than the other training methods.