Finite precision analysis of Support Vector Machine Classification in Logarithmic Number Systems

Proceedings of the EUROMICRO Systems on Digital System Design, DSD 2004

Volumn , Issue , 2004, Pages 254-261

  Khan, Faisal M ^a   Arnold, Mark G ^a   Pottenger, William M ^a  

^a Lehigh University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATASETS; KERNELS; LOGARITHMIC NUMBER SYSTEMS (LNS); SUPPORT VECTOR MACHINE (SVM);

ALGORITHMS; COGNITIVE SYSTEMS; COMPUTATIONAL COMPLEXITY; COMPUTER ARCHITECTURE; COMPUTER HARDWARE; COMPUTER SOFTWARE; DATA REDUCTION; DECISION MAKING; LEARNING SYSTEMS; NEURAL NETWORKS;

COMPUTER TERMINALS;

EID: 13944283804     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DSD.2004.1333285     Document Type: Conference Paper

References (24)

1. Davide Anguita, Andrea Boni and Sandro Ridella. "A Digital Architecture for Support Vector Machines: Theory, Algorithm, and FPGA Implementation." IEEE Transactions on Neural Networks, vol. 14, no. 5, pp. 993-1009, Sept. 2003.
2. Mark Arnold, Thomas Bailey, J. Cowles and Jerry Cupal. "Implementing Back Propagation Neural Nets with Logarithmic Arithmetic." Proceedings International. AMSE Conference on Neural Networks, San Diego, CA, May 29-31, (G. Mesnard and R. Swiniarski, Editors), vol. 1, pp. 75-86, 1991.
3. Mark Arnold, Thomas Bailey, Jerry Cupal and Mark Winkel. "On the Cost Effectiveness of Logarithmic Arithmetic for Back Propagation Training on SIMD Processors." International Conference on Neural Networks, Houston, Texas, pp. 933-936, June 9-12, 1997.
4. Mark Arnold. "Slide Rules for the 21st Century: Logarithmic Arithmetic as a High-speed, Low-cost, Low-power Alternative to Fixed Point Arithmetic." OSEE: Second Online Symposium for Electronics Engineers, 2001.
5. Mark Arnold. "Reduced Power Consumption for MPEG Decoding with LNS." Application Specific Architectures and Processors, San Jose, pp. 65-75, July 17-19, 2002.
6. Christopher Burges. "A Tutorial on Support Vector Machines for Pattern Recognition." Knowledge Discovery and Data Mining, vol. 2, pp. 121-167, 1998.
7. Gert Cauwenberghs, Editor. "Learning on Silicon: A Survey," Learning on Silicon: Adaptive VLSI Neural Systems. Boston: Kluwer Academic Publishers, 1999.
8. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: A Library for Support Vector Machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
9. Roman Genov and Gert Cauwenberghs. "Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines." Advances in Neural Information Processing Systems (NIPS'2001), Cambridge, MA: MIT Press, vol. 14, pp. 1099-1105, 2002.
10. Roman Genov and Gert Cauwenberghs. "Kerneltron: Support Vector Machine in Silicon. " IEEE Transactions on Neural Networks, vol. 14, no. 5, pp. 1426-1434, 2003.
11. Roman Genov, Shantanu Chakrabartty and Gert Cauwenberghs. "Silicon Support Vector Machine with On-Line Learning." International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 3, pp. 385-404, 2003.
12. Marti Hearst. "Support Vector Machines. " IEEE Intelligent Systems, vol. 13, no. 4, pp. 18-28 July 1998.
13. Ralf Herbich. Learning Kernel Classifiers: Theory and Algorithms. Cambridge: The MIT Press, 2002.
14. Jordan Holt and Jeng-Neng Hwang. "Finite Precision Error Analysis of Neural Network Hardware Implementations. " IEEE Transactions on Computers, vol. 42, no. 3, pp. 281-290, Mar 1993.
15. Chih-Wei Hsu, Chih-Chung Chang and Chih-Jen Li. "A Practical Guide to Support Vector Classification," available www.csie.ntu.edu.tw/~cjlin/ papers/guide/guide.pdf
16. Philip Leong and Marwan Jabri, "A Low Power VLSI Arrhythmia Classifier." IEEE Transactions on Neural Networks, vol. 6, no. 6, pp. 1435-1445, November 1995.
17. Victo Mak, Kuo Chu Lee and Ophir Frieder. Exploiting Parallelism in Pattern Matching: An Information Retrieval Application. ACM Transactions on Information Systems (TOIS), pp. 52-72, 1991.
18. Tony Martinez, Douglas Campbell, and Brent Hughes. "Priority ASOCS." Journal of Artificial Neural Networks, vol. 1, no. 3, pp. 403-429, 1994.
19. Bernhard Scholkopf, Alex Smola, and Klaus-Robert Muller. "Support Vector Methods in Learning and Feature Extraction." Australian Journal of Intelligent Information Processing Systems, vol. 1, pp. 3-9, 1998.
20. Alex Smola and Bernhard Scholkopf. "A Tutorial on Support Vector Regression." ESPRIT Working Group in Neural and Computational Learning II, NeuroCOLT2, Technical Report TR-98-030, 1998.
21. Thanos Stouraitis and Fred J. Taylor. "Analysis of Logarithmic Number System Processors." IEEE Transactions on Circuits and Systems, vol. 35, no. 5, pp. 519-527, May 1998.
22. Matthew Stout, George Rudolph, Tony Martinez, and Linton Salmon. "A VLSI Implementation of a Parallel, Self-Organizing Learning Model." Proceedings of the International Conference on Pattern Recognition, vol. 3, pp. 373-376, 1994.
23. Vladimir Vapnik. The Nature of Statistical Learning Theory, Springer Verlag, 1995.
24. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. New York: Morgan Kaufmann Publishers, 2000.