Silicon support vector machine with on-line learning

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 17, Issue 3, 2003, Pages 385-404

  Genov, Roman ^a   Chakrabartty, Shantanu ^b   Cauwenberghs, Gert ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b Johns Hopkins University   (United States)

Author keywords

Analog array processors; Kernel machines; Large margin classifiers; Matrix vector multiplication (MVM); Pattern recognition; Quadratic programming (QP); Sequential minimum optimization (SMO); Support vector machine (SVM)

Indexed keywords

COMPUTER HARDWARE; LEARNING ALGORITHMS; ONLINE SYSTEMS; PATTERN RECOGNITION; PROBLEM SOLVING; QUADRATIC PROGRAMMING; SILICON; VIDEO SIGNAL PROCESSING; VLSI CIRCUITS;

SUPPORT VECTOR MACHINES (SVM);

LEARNING SYSTEMS;

EID: 0038266787     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001403002472     Document Type: Conference Paper

References (23)

1. D. Anguita, S. Ridella and S. Rovetta, "Circuital implementation of support vector machines," Electron. Lett. 34, 16 (1998) 1596-1597.
2. D. P. Bertsekas, Nonlinear Programming, Athena Scientific, Belmont MA, 1995.
3. B. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifier," Proc. Fifth Annual ACM Workshop on Computational Learning Theory, 1992, pp. 144-152.
4. G. Cauwenberghs and M. Bayoumi, Learning on Silicon, Analog VLSI Adaptive Systems, Kluwer Academic, Norwell MA, 1999.
5. G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," Adv. Neural Information Processing Systems, Proc. 2000 IEEE NIPS Conf., MIT Press, Cambridge, MA, 2001, pp. 409-415.
6. T.-T. Friessand, N. Cristianini and C. Campbell, "The kernel adatron algorithm: a fast and simple learning procedure for support vector machines," Proc. 15th Int. Conf. Machine Learning (ICML98), 1998, pp. 188-196.
7. R. Genov and G. Cauwenberghs, "Charge-mode parallel architecture for matrix-vector multiplication," IEEE Trans. Circuits Syst. II 48, 10 (2001) 930-936.
8. R. Genov and G. Cauwenberghs, "Stochastic mixed-signal VLSI architecture for high-dimensional kernel machines," Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, Vol. 14, 2002, pp. 1099-1105.
9. F. Girosi, M. Jones and T. Poggio, "Regularization theory and neural networks architectures," Neural Comput. 7 (1995) 219-269.
10. M. Howes and D. Morgan (eds.), Charge-Coupled Devices and Systems, John Wiley, 1979.
11. T. Joachims, "Making large-scale SVM learning practical," Advances in Kernel Methods - Support Vector Learning, eds. B. Schölkopf, C. Burges and A. Smola, MIT Press, 1999, pp. 169-184.
12. S. Kang and S.-W. Lee, "Handheld computer vision system for the visually impaired," Proc. 3rd Int. Workshop on Human-Friendly Welfare Robotic Systems, Daejeon, Korea, 2002, pp. 43-48.
13. S. Keerthi, C. Bhattacharyya and K. Murthy, "A fast iterative nearest point algorithm for support vector machine classifier design," Technical Report TR-ISL-99-03, Department of CSA, IISc, Bangalore, India, 1999.
14. J. Kivinen, A. J. Smola and R. C. Williamson, "On-line learning with kernels," Adv. Neural Information Processing Systems (NIPS '2001), Vol. 14, MIT Press, Cambridge, MA, 2002, pp. 785-792.
15. G. Mulliken, F. Adil, G. Cauwenberghs and R. Genov, "Delta-sigma algorithmic analog-to-digital conversion," IEEE Int. Symp. Circuits and Systems (ISCAS'02), Scottsdale, AZ, May 26-29, 2002, ISCAS'2002.
16. C. Neugebauer and A. Yariv, "A parallel analog CCD/CMOS neural network IC," Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN'91), Seattle, WA, Vol. 1, 1991, pp. 447-451.
17. M. Oren, C. Papageorgiou, P. Sinha, E. Osuna and T. Poggio, "Pedestrian detection using wavelet templates," Computer Vision and Pattern Recognition, 1997, pp. 193-199.
18. E. Osuna, R. Freund and F. Girosi, "Training support vector machines: an application to face detection," Computer Vision and Pattern Recognition, 1997, pp. 130-136.
19. C. P. Papageorgiou, M. Oren and T. Poggio, "A general framework for object detection," Proc. Int. Conf. Computer Vision, 1998, pp. 555-562.
20. V. Pedroni, A. Agranat, C. Neugebauer and A. Yariv, "Pattern matching and parallel processing with CCD technology," Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN'92), Vol. 3, 1992, pp. 620-623.
21. J. Platt, "Fast training of support vector machines using sequential minimal optimization," Advances in Kernel Methods - Support Vector Learning, eds. B. Schölkopf, C. Burges and A. Smola, MIT Press, Cambridge, MA, 1999, pp. 185-208.
22. B. Schölkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi, T. Poggio and V. Vapnik, "Comparing support vector machines with Gaussian kernels to radial basis functions classifiers," IEEE Trans. Sign. Process. 45, 11 (1997) 2758-2765.
23. V. Vapnik, The Nature of Statistical Learning Theory, Springer Verlag, 1995.