Scaling Up Machine Learning
Parallel and Distributed Approaches
Herausgeber: Bekkerman, Ron; Langford, John; Bilenko, Mikhail
Scaling Up Machine Learning
Parallel and Distributed Approaches
Herausgeber: Bekkerman, Ron; Langford, John; Bilenko, Mikhail
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This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
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This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- Seitenzahl: 492
- Erscheinungstermin: 1. Januar 2012
- Englisch
- Abmessung: 260mm x 183mm x 31mm
- Gewicht: 1114g
- ISBN-13: 9780521192248
- ISBN-10: 0521192242
- Artikelnr.: 33868458
- Verlag: Cambridge University Press
- Seitenzahl: 492
- Erscheinungstermin: 1. Januar 2012
- Englisch
- Abmessung: 260mm x 183mm x 31mm
- Gewicht: 1114g
- ISBN-13: 9780521192248
- ISBN-10: 0521192242
- Artikelnr.: 33868458
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.
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.
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.