Optimizing 1-nearest prototype classifiers

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2013, Pages 460-467

  Wohlhart, Paul ^a   Kostinger, Martin ^a   Donoser, Michael ^a   Roth, Peter M ^a   Bischof, Horst ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Classification; Discriminative Prototype Learning; Machine Learning; Nearest Neighbor Classification; Optimization

Indexed keywords

CONSTANT IMPROVEMENT; LARGE-SCALE DATASETS; LINEAR CLASSIFIERS; NEAREST NEIGHBOR CLASSIFICATION; NEAREST NEIGHBOR CLASSIFIER; PROTOTYPE CLASSIFICATION; PROTOTYPE CLASSIFIERS; PROTOTYPE LEARNING;

ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION; PATTERN RECOGNITION;

CLASSIFICATION (OF INFORMATION);

EID: 84887349759     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2013.66     Document Type: Conference Paper

References (19)

1. O. Boiman, E. Shechtman, and M. Irani. In defense of Nearest-Neighbor based image classification. In Proc. CVPR, 2008.
2. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology, 2:27:1-27:27, 2011.
3. K. Crammer, R. Gilad-bachrach, A. Navot, and N. Tishby. Margin analysis of the LVQ algorithm. In Advances NIPS, 2002.
4. D. M. Dunlavy, T. G. Kolda, and E. Acar. Poblano v1. 0: A matlab toolbox for gradient-based optimization. Technical Report SAND2010-1422, Sandia National Laboratories, Albuquerque, NM and Livermore, CA, Mar. 2010.
5. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. JMLR, 9:1871-1874, 2008.
6. P. W. Frey and D. J. Slate. Letter recognition using hollandstyle adaptive classifiers. Machine Learning, 6(2):161-182, 1991.
7. J. Hull. A database for handwritten text recognition research. IEEE Trans. on PAMI, 16(5):550-554, May 1994.
8. T. Kohonen. Self-organization and associative memory. Springer-Verlag, New York, 1989.
9. L. Ladicky and P. H. S. Torr. Locally linear support vector machines. In Proc. ICML, 2011.
10. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, Nov 1998.
11. S. Maji, A. C. Berg, and J. Malik. Classification using intersection kernel support vector machines is efficient. In Proc. CVPR, 2008.
12. D. Ramanan and S. Baker. Local distance functions: A taxonomy, new algorithms, and an evaluation. IEEE Trans. on PAMI, 33:794-806, 2011.
13. S. Seo and K. Obermayer. Soft learning vector quantization. Neural Computation, 2002.
14. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In Proc. ICML, 2007.
15. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In Proc. CVPR, 2011.
16. A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. IEEE Trans. on PAMI, 34(3), 2011.
17. K. Weinberger and L. Saul. Distance metric learning for large margin nearest neighbor classification. JMLR, 10:207-244, 2009.
18. Z. Zhang, P. Sturgess, S. Sengupta, N. Crook, and P. H. S. Torr. Efficient discriminative learning of parametric nearest neighbor classifiers. In Proc. CVPR, 2012.
19. X. Zhu, C. Vondrick, D. Ramanan, and C. C. Fowlkes. Do we need more training data or better models for object detection? In Proc. BMVC, 2012.