The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Mixture for Classification and Regression Problems
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Predicting the disulfide bonding state of cysteines with combinations of kernel machines
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Comments on "A parallel mixture of SVMs for very large scale problems"
Neural Computation
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Core Vector Regression for very large regression problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Information Sciences: an International Journal
Training a Support Vector Machine in the Primal
Neural Computation
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Towards Effective Visual Data Mining with Cooperative Approaches
Visual Data Mining
Fast Support Vector Data Description Using K-Means Clustering
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Nonlinear clustering-based support vector machine for large data sets
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Separating hypersurfaces of SVMs in input spaces
Pattern Recognition Letters
CombNET-III with Nonlinear Gating Network and Its Application in Large-Scale Classification Problems
IEICE - Transactions on Information and Systems
A Parallel Implementation of Error Correction SVM with Applications to Face Recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Fast Local Support Vector Machines for Large Datasets
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A fast SVM training method for very large datasets
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
The Journal of Machine Learning Research
Sparse approximation through boosting for learning large scale kernel machines
IEEE Transactions on Neural Networks
Fuzzy integral to speed up support vector machines training for pattern classification
International Journal of Knowledge-based and Intelligent Engineering Systems
Condensed vector machines: learning fast machine for large data
IEEE Transactions on Neural Networks
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
Parallel learning to rank for information retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Parallel randomized support vector machine
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mixture of SVMs for face class modeling
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Efficient astronomical data classification on large-scale distributed systems
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Parallel tuning of support vector machine learning parameters for large and unbalanced data sets
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
Data mining with parallel support vector machines for classification
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
z-SVM: an SVM for improved classification of imbalanced data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
A MapReduce-based distributed SVM algorithm for automatic image annotation
Computers & Mathematics with Applications
Hierarchical linear support vector machine
Pattern Recognition
Training support vector machine through redundant data reduction
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Parallel multitask cross validation for Support Vector Machine using GPU
Journal of Parallel and Distributed Computing
Support vector machine for large databases as classifier
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
CloudSVM: training an SVM classifier in cloud computing systems
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Indexed block coordinate descent for large-scale linear classification with limited memory
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Meta-ELM: ELM with ELM hidden nodes
Neurocomputing
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Support vector machines (SVMs) are the state-of-the-art models for many classification problems, but they suffer from the complexity of their training algorithm, which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundred thousand examples with SVMs. This article proposes a new mixture of SVMs that can be easily implemented in parallel and where each SVM is trained on a small subset of the whole data set. Experiments on a large benchmark data set (Forest) yielded significant time improvement (time complexity appears empirically to locally grow linearly with the number of examples). In addition, and surprisingly, a significant improvement in generalization was observed.