A distributed SVM for image annotation

Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

Volumn 6, Issue , 2010, Pages 2983-2987

  Alham, Nasullah Khalid ^a   Li, Maozhen ^a   Hammoud, Suhel ^a   Liu, Yang ^a   Ponraj, Mahesh ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Distributed SVM; Image anotation; Machine learning; MapReduce; SMO

Indexed keywords

DISTRIBUTED SVM; IMAGE ANOTATION; MACHINE-LEARNING; MAP-REDUCE; SMO;

FUZZY SETS;

SUPPORT VECTOR MACHINES;

EID: 78649272614     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FSKD.2010.5569084     Document Type: Conference Paper

References (21)

1. N. Khalid Alham, M. Li, S. Hammoud and H. Qi, Evaluating Machine Learning Techniques for Automatic Image Annotations, Proc. of FSKD09, IEEE CS, Aug. 2009
2. D. Bickson, E. Yom-Tov, and D. Dolev, "A Gaussian Belief Propagation Solver for Large Scale Support Vector Machines," 2008.
3. L. Cao, S. Keerthi, Chong-Jin Ong, J. Zhang, U. Periyathamby, Xiu Ju Fu, and H. Lee, "Parallel sequential minimal optimization for the training of support vector machines," Neural Networks, IEEE Transactions on, vol. 17, 2006, pp. 1039-1049.
4. Apache Hadoop! Available at: http://hadoop.apache.org/ [Accessed November 2, 2009
5. S. Ghemawat, H. Gobioff, and S. Leung, "The Google file system," SIGOPS Oper. Syst. Rev., vol. 37, 2003, pp. 29-43.
6. R. Lämmel, "Google's MapReduce programming model - Revisited," Sci. Comput. Program., vol. 68, 2007, pp. 208-237.
7. R. Collobert, S. Bengio, and Y. Bengio, "A Parallel Mixture of SVMs for Very Large Scale Problems," 2002.
8. G. Zanghirati and L. Zanni, "A parallel solver for large quadratic programs in training support vector machines," Parallel Comput., vol. 29, 2003, pp. 535-551.
9. L. Smith, M. Bull, Development of mixed mode MPI / OpenMP applications, IOS Press Amsterdam, The Netherlands, The Netherlands.
10. B. Catanzaro, N. Sundaram, and K. Keutzer, "Fast support vector machine training and classification on graphics processors," Proceedings of the 25th international conference on Machine learning, Helsinki, Finland: ACM, 2008, pp. 104-111.
11. D. Kun. L. Yih. A. Perera, Parallel SMO for Training Support Vector Machines. SMA 5505 Project Final Report. May 2003. Available at www.geocities.com/asankha/Contents/Education/APC-SVM.pdf [accessed June 29, 2009]
12. Weka 3 - Data Mining with Open Source Machine Learning Software in Java. Available at: http://www.cs.waikato.ac.nz/ml/weka/ [Accessed November 2, 2009].
13. S. Abe, Support Vector Machines for Pattern Classification (Advances in Pattern Recognition), Springer-Verlag New York, Inc., 2005.
14. T. Sikora: The MPEG-7 visual standard for content description-an overview. IEEE Trans. Circuits Syst. Video Techn. (2001) 11(6): 696-702
15. The Cilk Project. Available at: http://supertech.csail.mit.edu/cilk/ [Accessed November 29, 2009].
16. M. Frigo, C. Leiserson, K. Randall, The Implementation of the Cilk-5 Multithreaded Language. Proceedings of the SIGPLAN '98 Conference on Programming Language Design and Implementation, pp. 212-223, Montreal, Quebec, Canada.
17. J. Venner, Pro Hadoop, Springer 2009, pp 1-89.
18. J.C. Platt, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines," 1998.
19. E. Osuna, R. Freund, and F. Girosit, "Training support vector machines: an application to face detection," Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 1997, pp. 130-136.
20. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, and K.R.K. Murthy, "Improvements to Platt's SMO Algorithm for SVM Classifier Design," Neural Comput., vol. 13, 2001, pp. 637-649.
21. Nello Cristianini and J. Shawe-Taylor, An introduction to support vector machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.