Learning to recognize 3D objects with SNoW

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1842, Issue , 2000, Pages 439-454

  Yang, Ming Hsuan ^a   Roth, Dan ^a   Ahuja, Narendra ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LINEAR NETWORKS; OBJECT RECOGNITION; OBJECT-ORIENTED DATABASES; SNOW; SUPPORT VECTOR MACHINES;

3D OBJECT RECOGNITION; COMPUTATIONAL COSTS; LARGE-SCALE OBJECTS; LEARNING ARCHITECTURES; LINEAR FUNCTIONS; NEAREST NEIGHBORS; SPARSE NETWORK; SUPPORT VECTOR MACHINE (SVMS);

LEARNING ALGORITHMS;

EID: 84944184907     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45054-8_29     Document Type: Conference Paper

References (21)

1. A. Blum. Learning boolean functions in an infinite attribute space. Machine Learning, 9(4):373-386, Oct. 1992.
2. J. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986.
3. A. Carleson, C. Cumby, J. Rosen, and D. Roth. The SNoW learning architecture. Technical Report UIUCDCS-R-99-2101, UIUC Computer Science Department, May 1999.
4. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20, 1995.
5. A. R. Golding and D. Roth. A winnow based approach to context-sensitive spelling correction. Machine Learning, 34:107-130, 1999. Special Issue on Machine Learning and Natural Language.
6. M. Kearns and U. Vazirani. Introduction to computational Learning Theory. MIT Press, 1994.
7. J. Kivinen and M. K. Warmuth. Exponentiated gradient versus gradient descent for linear predictors. In Proceedings of the Annual ACM Symp. on the Theory of Computing, 1995.
8. N. Littlestone. Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm. Machine Learning, 2:285-318, 1988.
9. N. Littlestone. Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow. In Proc. 4th Annu. Workshop on Comput. Learning Theory, pages 147-156, San Mateo, CA, 1991. Morgan Kaufmann.
10. M. Munoz, V. Punyakanok, D. Roth, and D. Zimak. A learning approach to shallow parsing. In EMNLP-VLC'99, the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, June 1999.
11. H. Murase and S. K. Nayar. Visual learning and recognition of 3-d objects from appearance. International Journal of Computer Vision, 14:5-24, 1995.
12. S. K. Nayar, S. A. Nene, and H. Murase. Real-time 100 object recognition system. In Proceedings of IEEE International Conference on Robotics and Automation, 1996.
13. T. Poggio and S. Edelman. A network that learns to recognize 3d objects. Nature, 343:263-266, 1990.
14. M. Pontil and A. Verri. Support vector machines for 3d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6):637-646, 1998.
15. D. Roobaert and M. V. Hulle. View-based 3d object recognition with support vector machines. In IEEE International Workshop on Neural Networks for Signal Processing, 1999.
16. D. Roth. Learning to resolve natural language ambiguities: A unified approach. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 806-813, 1998.
17. B. Schölkopf. Support Vector Learning. PhD thesis, Informatik der Technischen Universitat Berlin, 1997.
18. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86, 1991.
19. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, Nov. 1984.
20. V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.
21. M.-H. Yang, D. Roth, and N. Ahuja. A SNoW-based face detector. In NIPS12; The 1999 Conference on Advances in Neural Information Processing Systems, pages 855-861. MIT Press, 2000