A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
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
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Training of Support Vector Machines by Extracting Boundary Data
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Fast pattern selection for support vector classifiers
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Data Selection Using SASH Trees for Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Closest pairs data selection for support vector machines
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Selecting training points for one-class support vector machines
Pattern Recognition Letters
Query sampling for learning data fusion
Proceedings of the 20th ACM international conference on Information and knowledge management
Support vector machines training data selection using a genetic algorithm
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In recent years, support vector machines (SVMs) have become a popular tool for pattern recognition and machine learning. Training a SVM involves solving a constrained quadratic programming problem, which requires large memory and enormous amounts of training time for large-scale problems. In contrast, the SVM decision function is fully determined by a small subset of the training data, called support vectors. Therefore, it is desirable to remove from the training set the data that is irrelevant to the final decision function. In this paper we propose two new methods that select a subset of data for SVM training. Using real-world datasets, we compare the effectiveness of the proposed data selection strategies in terms of their ability to reduce the training set size while maintaining the generalization performance of the resulting SVM classifiers. Our experimental results show that a significant amount of training data can be removed by our proposed methods without degrading the performance of the resulting SVM classifiers.