BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Learning concepts from large scale imbalanced data sets using support cluster machines
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Fast BMU Search for Support Vector Machine
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
An online incremental learning support vector machine for large-scale data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Using the leader algorithm with support vector machines for large data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Clustering and categorization of Brazilian portuguese legal documents
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
The Journal of Machine Learning Research
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For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.