Scaling Up Machine Learning

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

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.