Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- 1 Scaling Up Machine Learning: Introduction
- Part One Frameworks for Scaling Up Machine Learning
- Part Two Supervised and Unsupervised Learning Algorithms
- Part Three Alternative Learning Settings
- 14 Parallel Online Learning
- 15 Parallel Graph-Based Semi-Supervised Learning
- 16 Distributed Transfer Learning via Cooperative Matrix Factorization
- 17 Parallel Large-Scale Feature Selection
- Part Four Applications
- Subject Index
- References
14 - Parallel Online Learning
from Part Three - Alternative Learning Settings
Published online by Cambridge University Press: 05 February 2012
Book contents
- Frontmatter
- Contents
- Contributors
- Preface
- 1 Scaling Up Machine Learning: Introduction
- Part One Frameworks for Scaling Up Machine Learning
- Part Two Supervised and Unsupervised Learning Algorithms
- Part Three Alternative Learning Settings
- 14 Parallel Online Learning
- 15 Parallel Graph-Based Semi-Supervised Learning
- 16 Distributed Transfer Learning via Cooperative Matrix Factorization
- 17 Parallel Large-Scale Feature Selection
- Part Four Applications
- Subject Index
- References
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Scaling up Machine LearningParallel and Distributed Approaches, pp. 283 - 306Publisher: Cambridge University PressPrint publication year: 2011
References
1967. A Theory of Adaptive Pattern Classifiers. IEEE Transactions on Electronic Computers, 16, 299–307.CrossRefGoogle Scholar
, , and 1999. Beating the Hold-out: Bounds for k-Fold and Progressive Cross-Validation. Pages 203–208 of: Proceedings of 12th Annual Conference on Computational Learning Theory. New York: ACM.Google Scholar
. 2008. Stochastic Gradient SVMs. http://leon.bottou.org/projects/sgd.
, and 1969. Applied Optimal Control: Optimization, Estimation, and Control. Blairsdell.Google Scholar
, , , , , , and 2007. Map-Reduce for Machine Learning on Multicore. In: Neural Information Processing Systems (NIPS) 19.Google Scholar
, , , and 2010. Optimal Distributed Online Prediction using Mini-Batches. In: Learning on Cores, Clusters, and Clouds Workshop.Google Scholar
, and 1992. Global Convergence Properties of Conjugate Gradient Methods for Optimization. SIAM Journal on Optimization, 2(1), 21–42.CrossRefGoogle Scholar
, , and 1995. Tight Worst-Case Loss Bounds for Predicting with Expert Advice. Pages 69–83 of: Computational Learning Theory: EuroColt '95. New York: Springer.CrossRefGoogle Scholar
, , and 2007. Vowpal Wabbit Online Learning Project. http://hunch.net/?p=309.
, , and 2008. Exploration Scavenging. In: Proceedings of International Conference on Machine Learning (ICML).CrossRefGoogle Scholar
, , and 2009. Slow Learners Fast. arXiv:0911.0491.
, , , and 2004. RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research, 5, 361–397.Google Scholar
, , , , and 2009. Efficient Large-Scale Distributed Training of Conditional Maximum EntropyModels. In: Neural Information Processing Systems (NIPS).Google Scholar
, , and 2010. Distributed Training Strategies for the Structured Perceptron. In: North American Association for Computational Linguistics (NAACL).Google Scholar
, and 2008. Random Features for Large-Scale Kernel Machines. In: , , , and (eds), Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.Google Scholar
1958. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65(6), 386–408.CrossRefGoogle Scholar
, , and 1986. Learning Internal Representations by Error Propagation. Pages 318–362 of: Parallel Distributed Processing. Cambridge, MA: MIT Press.Google Scholar
, , and 2007. Pegasos: Primal Estimated sub-GrAdient Solver for SVM. In: Proceedings of International Conference on Machine Learning.Google Scholar
, , , , , , and 2009. Hash Kernels. Society for Artificial Intelligence and Statistics.Google Scholar
, , , and 2009. BundleMethods for Regularized Risk Minimization. Journal of Machine Learning Research, 11, 311–365.Google Scholar
, , , , and 2009. Feature Hashing for Large Scale Multitask Learning. In: , and (eds), International Conference on Machine Learning.Google Scholar
- 6
- Cited by