On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
Machine Learning
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
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
Reducing the run-time complexity in support vector machines
Advances in kernel methods
Convex Optimization
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Feature hashing for large scale multitask learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Large linear classification when data cannot fit in memory
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An architecture for parallel topic models
Proceedings of the VLDB Endowment
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Selective block minimization for faster convergence of limited memory large-scale linear models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
The p-norm generalization of the LMS algorithm for adaptive filtering
IEEE Transactions on Signal Processing
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
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Modern computer hardware offers an elaborate hierarchy of storage subsystems with different speeds, capacities, and costs associated with them. Furthermore, processors are now inherently parallel offering the execution of several diverse threads simultaneously. This paper proposes StreamSVM, the first algorithm for training linear Support Vector Machines (SVMs) which takes advantage of these properties by integrating caching with optimization. StreamSVM works by performing updates in the dual, thus obviating the need to rebalance frequently visited examples. Furthermore we trade off file I/O with data expansion on the fly by generating features on demand. This significantly increases throughput. Experiments show that StreamSVM outperforms other linear SVM solvers, including the award winning work of [38], by orders of magnitude and produces more accurate solutions within a shorter amount of time.