An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
Mathematical Programming: Series A and B
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
A modified projection algorithm for large strictly-convex quadratic programs
Journal of Optimization Theory and Applications
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Variable projection methods for large convex quadratic programs
Recent trends in numerical analysis
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
A parallel mixture of SVMs for very large scale problems
Neural Computation
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
MPI: A Message-Passing Interface Standard
MPI: A Message-Passing Interface Standard
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming
Numerische Mathematik
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds
Mathematical Programming: Series A and B
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
A fast parallel optimization for training support vector machine
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
On the convergence of the decomposition method for support vector machines
IEEE Transactions on Neural Networks
Asymptotic convergence of an SMO algorithm without any assumptions
IEEE Transactions on Neural Networks
The Interplay of Optimization and Machine Learning Research
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
Optimized cutting plane algorithm for support vector machines
Proceedings of the 25th international conference on Machine learning
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 convergent hybrid decomposition algorithm model for SVM training
IEEE Transactions on Neural Networks
Parallel multiclass classification using SVMs on GPUs
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
The Journal of Machine Learning Research
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
The Journal of Machine Learning Research
Iterative regularization algorithms for constrained image deblurring on graphics processors
Journal of Global Optimization
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Evaluating point-based POMDP solvers on multicore machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
MSSVM: A Modular Solver for Support Vector Machines
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Linear support vector machines via dual cached loops
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A novel distributed machine learning method for classification: parallel covering algorithm
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Hi-index | 0.00 |
Parallel software for solving the quadratic program arising in training support vector machines for classification problems is introduced. The software implements an iterative decomposition technique and exploits both the storage and the computing resources available on multiprocessor systems, by distributing the heaviest computational tasks of each decomposition iteration. Based on a wide range of recent theoretical advances, relevant decomposition issues, such as the quadratic subproblem solution, the gradient updating, the working set selection, are systematically described and their careful combination to get an effective parallel tool is discussed. A comparison with state-of-the-art packages on benchmark problems demonstrates the good accuracy and the remarkable time saving achieved by the proposed software. Furthermore, challenging experiments on real-world data sets with millions training samples highlight how the software makes large scale standard nonlinear support vector machines effectively tractable on common multiprocessor systems. This feature is not shown by any of the available codes.