A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
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
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Neural Computation
Clustering based large margin classification: a scalable approach using SOCP formulation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Random Sampling for Continuous Streams with Arbitrary Updates
IEEE Transactions on Knowledge and Data Engineering
A new intrusion detection system using support vector machines and hierarchical clustering
The VLDB Journal — The International Journal on Very Large Data Bases
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Selecting training points for one-class support vector machines
Pattern Recognition Letters
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
Reducing SVM classification time using multiple mirror classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Survey of clustering algorithms
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
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Support Vector Machines SVMs are high-accuracy classifiers. However, normal SVM algorithms are unsuitable for classification of large data sets because of their training complexity. In this paper, we propose a novel SVM classification approach for large data sets. We first use the random selection to select a small group of training data for the first-stage SVM. Then a de-clustering technique is proposed to recover the training data for the second-stage SVM. This two-stage SVM classifier has distinctive advantages on dealing with huge data sets such as those in bioinformatics. The performance analysis is also given in this paper. Finally, we apply the proposed method on several benchmark problems. Experimental results demonstrate that this approach has good classification accuracy while the training is significantly faster than other SVM classifiers.