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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
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
Approximate minimum enclosing balls in high dimensions using core-sets
Journal of Experimental Algorithmics (JEA)
An Effective Support Vector Machines (SVMs) Performance Using Hierarchical Clustering
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Support vector machine classification based on fuzzy clustering for large data sets
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
A one-layer recurrent neural network for support vector machine learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Fast Modular network implementation for support vector machines
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
A study on SMO-type decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Kernel Matrix Learning for One-Class Classification
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A reduced data set method for support vector regression
Expert Systems with Applications: An International Journal
The application of support vector regression in the dual-axis tilt sensor modeling
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Design and implementation of e-journal review system using text-mining technology
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
A fast SVM training algorithm based on a decision tree data filter
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Multi-class classification with one-against-one using probabilistic extreme learning machine
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Detecting RNA sequences using two-stage SVM classifier
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Learning using privileged information in prototype based models
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Fast classification for large data sets via random selection clustering and Support Vector Machines
Intelligent Data Analysis
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Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.