The nature of statistical learning theory
The nature of statistical learning theory
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
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
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
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Training a support vector machines on a data set of huge size may suffer from the problem of slow training. In this paper, a clustering-based geometric support vector machines (CBGSVM) was proposed to resolve this problem, initial classes are got by k-means cluster, then develop a fast iterative algorithm for identifying the support vector machine of the centers of all subclasses. To speed up convergence, we initialize our algorithm with the nearest pair of the center from opposite classes, and then use an optimization-based approach to increment or prune the candidate support vector set. The algorithm makes repeated passes over the centers to satisfy the KKT constraints. The method speeds up the training process fast comparing with standard support vector machines under the almost same classification precision.