In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.
In many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1. Scaling up machine learning: introduction Ron Bekkerman Mikhail Bilenko and John Langford; Part I. Frameworks for Scaling Up Machine Learning: 2. Mapreduce and its application to massively parallel learning of decision tree ensembles Biswanath Panda Joshua S. Herbach Sugato Basu and Roberto J. Bayardo; 3. Large-scale machine learning using DryadLINQ Mihai Budiu Dennis Fetterly Michael Isard Frank McSherry and Yuan Yu; 4. IBM parallel machine learning toolbox Edwin Pednault Elad Yom-Tov and Amol Ghoting; 5. Uniformly fine-grained data parallel computing for machine learning algorithms Meichun Hsu Ren Wu and Bin Zhang; Part II. Supervised and Unsupervised Learning Algorithms: 6. PSVM: parallel support vector machines with incomplete Cholesky Factorization Edward Chang Hongjie Bai Kaihua Zhu Hao Wang Jian Li and Zhihuan Qiu; 7. Massive SVM parallelization using hardware accelerators Igor Durdanovic Eric Cosatto Hans Peter Graf Srihari Cadambi Venkata Jakkula Srimat Chakradhar and Abhinandan Majumdar; 8. Large-scale learning to rank using boosted decision trees Krysta M. Svore and Christopher J. C. Burges; 9. The transform regression algorithm Ramesh Natarajan and Edwin Pednault; 10. Parallel belief propagation in factor graphs Joseph Gonzalez Yucheng Low and Carlos Guestrin; 11. Distributed Gibbs sampling for latent variable models Arthur Asuncion Padhraic Smyth Max Welling David Newman Ian Porteous and Scott Triglia; 12. Large-scale spectral clustering with Mapreduce and MPI Wen-Yen Chen Yangqiu Song Hongjie Bai Chih-Jen Lin and Edward Y. Chang; 13. Parallelizing information-theoretic clustering methods Ron Bekkerman and Martin Scholz; Part III. Alternative Learning Settings: 14. Parallel online learning Daniel Hsu Nikos Karampatziakis John Langford and Alex J. Smola; 15. Parallel graph-based semi-supervised learning Jeff Bilmes and Amarnag Subramanya; 16. Distributed transfer learning via cooperative matrix factorization Evan Xiang Nathan Liu and Qiang Yang; 17. Parallel large-scale feature selection Jeremy Kubica Sameer Singh and Daria Sorokina; Part IV. Applications: 18. Large-scale learning for vision with GPUS Adam Coates Rajat Raina and Andrew Y. Ng; 19. Large-scale FPGA-based convolutional networks Clement Farabet Yann LeCun Koray Kavukcuoglu Berin Martini Polina Akselrod Selcuk Talay and Eugenio Culurciello; 20. Mining tree structured data on multicore systems Shirish Tatikonda and Srinivasan Parthasarathy; 21. Scalable parallelization of automatic speech recognition Jike Chong Ekaterina Gonina Kisun You and Kurt Keutzer.
1. Scaling up machine learning: introduction Ron Bekkerman Mikhail Bilenko and John Langford; Part I. Frameworks for Scaling Up Machine Learning: 2. Mapreduce and its application to massively parallel learning of decision tree ensembles Biswanath Panda Joshua S. Herbach Sugato Basu and Roberto J. Bayardo; 3. Large-scale machine learning using DryadLINQ Mihai Budiu Dennis Fetterly Michael Isard Frank McSherry and Yuan Yu; 4. IBM parallel machine learning toolbox Edwin Pednault Elad Yom-Tov and Amol Ghoting; 5. Uniformly fine-grained data parallel computing for machine learning algorithms Meichun Hsu Ren Wu and Bin Zhang; Part II. Supervised and Unsupervised Learning Algorithms: 6. PSVM: parallel support vector machines with incomplete Cholesky Factorization Edward Chang Hongjie Bai Kaihua Zhu Hao Wang Jian Li and Zhihuan Qiu; 7. Massive SVM parallelization using hardware accelerators Igor Durdanovic Eric Cosatto Hans Peter Graf Srihari Cadambi Venkata Jakkula Srimat Chakradhar and Abhinandan Majumdar; 8. Large-scale learning to rank using boosted decision trees Krysta M. Svore and Christopher J. C. Burges; 9. The transform regression algorithm Ramesh Natarajan and Edwin Pednault; 10. Parallel belief propagation in factor graphs Joseph Gonzalez Yucheng Low and Carlos Guestrin; 11. Distributed Gibbs sampling for latent variable models Arthur Asuncion Padhraic Smyth Max Welling David Newman Ian Porteous and Scott Triglia; 12. Large-scale spectral clustering with Mapreduce and MPI Wen-Yen Chen Yangqiu Song Hongjie Bai Chih-Jen Lin and Edward Y. Chang; 13. Parallelizing information-theoretic clustering methods Ron Bekkerman and Martin Scholz; Part III. Alternative Learning Settings: 14. Parallel online learning Daniel Hsu Nikos Karampatziakis John Langford and Alex J. Smola; 15. Parallel graph-based semi-supervised learning Jeff Bilmes and Amarnag Subramanya; 16. Distributed transfer learning via cooperative matrix factorization Evan Xiang Nathan Liu and Qiang Yang; 17. Parallel large-scale feature selection Jeremy Kubica Sameer Singh and Daria Sorokina; Part IV. Applications: 18. Large-scale learning for vision with GPUS Adam Coates Rajat Raina and Andrew Y. Ng; 19. Large-scale FPGA-based convolutional networks Clement Farabet Yann LeCun Koray Kavukcuoglu Berin Martini Polina Akselrod Selcuk Talay and Eugenio Culurciello; 20. Mining tree structured data on multicore systems Shirish Tatikonda and Srinivasan Parthasarathy; 21. Scalable parallelization of automatic speech recognition Jike Chong Ekaterina Gonina Kisun You and Kurt Keutzer.
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