Map-reduce for machine learning on multicore

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 281-288

  Chu, Cheng Tao ^a   Kim, Sang Kyun ^a   Lin, Yi An ^a   Yu, Yuan Yuan ^a   Bradski, Gary ^a,b   Ng, Andrew Y ^a   Olukotun, Kunle ^a  

^a STANFORD UNIVERSITY   (United States)

^b Rexee Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIANS; K-MEANS; LINEAR SPEED-UP; LOGISTIC REGRESSIONS; MANY CORE; MAP-REDUCE; MULTI CORE; NAIVE BAYES; PROGRAMMING FRAMEWORK; STATISTICAL QUERY MODELS;

COMPUTER ARCHITECTURE; DISCRIMINANT ANALYSIS; LEARNING SYSTEMS; LOGISTICS; PARALLEL PROGRAMMING;

LEARNING ALGORITHMS;

EID: 56049109090     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Sejnowski TJ. Bell AJ. An information-maximization approach to blind separation and blind deconvolution. In Neural Computation, 1995.
2. O. Chapelle. Training a support vector machine in the primal. Journal of Machine Learning Research (submitted), 2006.
3. W. S. Cleveland and S. J. Devlin. Locally weighted regression: An approach to regression analysis by local fitting. In J. Amer. Statist. Assoc. 83, pages 596-610, 1988.
4. L. Csanky. Fast parallel matrix inversion algorithms. SIAM J. Comput., 5(4):618-623, 1976.
5. A. Silvescu D. Caragea and V. Honavar. A framework for learning from distributed data using sufficient statistics and its application to learning decision trees. International Journal of Hybrid Intelligent Systems, 2003.
6. R. J. Williams D. E. Rumelhart, G. E. Hinton. Learning representation by back-propagating errors. In Nature, volume 323, pages 533-536, 1986.
7. J. Dean and S. Ghemawat. Mapreduce: Simplified data processing on large clusters. Operating Systems Design and Implementation, pages 137-149, 2004.
8. N.M. Dempster A.P., Laird and Rubin D.B.
9. D.J. Frank. Power-constrained cmos scaling limits. IBM Journal of Research and Development, 46, 2002.
10. P. Gelsinger. Microprocessors for the new millennium: Challenges, opportunities and new frontiers. In ISSCC Tech. Digest, pages 22-25, 2001.
11. Leon Bottou Igor Durdanovic Hans Peter Graf, Eric Cosatto and Vladimire Vapnik. Parallel support vector machines: The cascade svm. In NIPS, 2004.
12. J. Hartigan. Clustering Algorithms. Wiley, 1975.
13. T. Hastie and R. Tibshirani. Discriminant analysis by gaussian mixtures. Journal of the Royal Statistical Society B, pages 155-176, 1996.
14. R. Jin and G. Agrawal. Shared memory parallelization of data mining algorithms: Techniques, programming interface, and performance. In Second SIAM International Conference on Data Mining, 2002.
15. M. Kearns. Efficient noise-tolerant learning from statistical queries. pages 392-401, 1999.
16. Michael Kearns and Umesh V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994.
17. David Lewis. Naive (bayes) at forty: The independence asssumption in information retrieval. In ECML98: Tenth European Conference On Machine Learning, 1998.
18. Kun Liu and Hillow Kargupta. Distributed data mining bibliography. http://www.cs.umbc.edu/ hillol/DDMBIB/, 2006.
19. T. K. MOON. The expectation-maximization algorithm. In IEEE Trans. Signal Process, pages 47-59, 1996.
20. G. Moore. Progress in digital integrated electronics. In IEDM Tech. Digest, pages 11-13, 1975.
21. Wayne Iba Pat Langley and Kevin Thompson. An analysis of bayesian classifiers. In AAAI, 1992.
22. John C. Platt. Fast training of support vector machines using sequential minimal optimization. pages 185-208, 1999.
23. Daryl Pregibon. Logistic regression diagnostics. In The Annals of Statistics, volume 9, pages 705-724, 1981.
24. T. Studt. There's a multicore in your future, http://tinyurl.com/ohd2m, 2006.
25. Herb Sutter and James Larus. Software and the concurrency revolution. Queue, 3(7):54-62, 2005.
26. L.G. Valiant. A theory of the learnable. Communications of the ACM, 3(11):1134-1142, 1984.
27. V. Vapnik. Estimation of Dependencies Based on Empirical Data. Springer Verlag, 1982.
28. R. E. Welsch and E. KUH. Linear regression diagnostics. In Working Paper 173, Nat. Bur. Econ. Res.Inc, 1977.
29. K. Esbensen Wold, S. and P. Geladi. Principal component analysis. In Chemometrics and Intelligent Laboratory Systems, 1987.