A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
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Advances in kernel methods
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Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Prediction games and arcing algorithms
Neural Computation
Towards scalable support vector machines using squashing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A parallel mixture of SVMs for very large scale problems
Neural Computation
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Random Sampling Technique for Training Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Lagrangian support vector machines
The Journal of Machine Learning Research
Efficient svm training using low-rank kernel representations
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
A tutorial on support vector regression
Statistics and Computing
An Effective Support Vector Machines (SVMs) Performance Using Hierarchical Clustering
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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
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IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper presents a kernel clustering-based support vector machine (KCB-SVM) that generalizes the linear clustering-based support vector machine (CB-SVM) to solve nonlinear classification problems in a novel way. It can not only handles large data sets, but can also have nonlinear discriminant power. By introducing kernel clustering, KCB-SVM unifies the metrics in the clustering and training stages. Elaborately designed clustering features summarize all the information required for further clustering and training, which allows only one scan of the total data sets. Experiments on both artificial and real large data sets show that the KCB-SVM not only achieves better classification accuracy than random sampling, active learning and CB-SVM, but also retains the ability to handle large data sets.