Object-oriented software for quadratic programming
ACM Transactions on Mathematical Software (TOMS)
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental approximate matrix factorization for speeding up support vector machines
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A convergent decomposition algorithm for support vector machines
Computational Optimization and Applications
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Cutting-plane training of structural SVMs
Machine Learning
Fast and efficient strategies for model selection of Gaussian support vector machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A convergent hybrid decomposition algorithm model for SVM training
IEEE Transactions on Neural Networks
Multi-Standard Quadratic Optimization: interior point methods and cone programming reformulation
Computational Optimization and Applications
Hybrid MPI/OpenMP Parallel Linear Support Vector Machine Training
The Journal of Machine Learning Research
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
The Journal of Machine Learning Research
Computational Optimization and Applications
Using an iterative linear solver in an interior-point method for generating support vector machines
Computational Optimization and Applications
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
The Journal of Machine Learning Research
Exploiting separability in large-scale linear support vector machine training
Computational Optimization and Applications
Large Linear Classification When Data Cannot Fit in Memory
ACM Transactions on Knowledge Discovery from Data (TKDD)
Adaptive constraint reduction for convex quadratic programming
Computational Optimization and Applications
Expert Systems with Applications: An International Journal
Matrix-free interior point method
Computational Optimization and Applications
Malware characteristics and threats on the internet ecosystem
Journal of Systems and Software
Mathematical and Computer Modelling: An International Journal
Review: Supervised classification and mathematical optimization
Computers and Operations Research
Smoothing multivariate performance measures
The Journal of Machine Learning Research
Computers in Biology and Medicine
The Journal of Machine Learning Research
Computational Optimization and Applications
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We investigate the use of interior-point methods for solving quadratic programming problems with a small number of linear constraints, where the quadratic term consists of a low-rank update to a positive semidefinite matrix. Several formulations of the support vector machine fit into this category. An interesting feature of these particular problems is the volume of data, which can lead to quadratic programs with between 10 and 100 million variables and, if written explicitly, a dense Q matrix. Our code is based on OOQP, an object-oriented interior-point code, with the linear algebra specialized for the support vector machine application. For the targeted massive problems, all of the data is stored out of core and we overlap computation and input/output to reduce overhead. Results are reported for several linear support vector machine formulations demonstrating that the method is reliable and scalable.