Group-induced vector spaces

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

Volumn 4472 LNCS, Issue , 2007, Pages 190-199

  Bicego, Manuele ^a,d   Pȩkalska, Elzbieta ^b   Duin, Robert P W ^c  

^a UNIVERSITY OF SASSARI   (Italy)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d NONE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONFORMAL MAPPING; DATA STRUCTURES; FUNCTION EVALUATION; LINEAR ALGEBRA;

GROUP-INDUCED VECTOR SPACES (GIVS); PROXIMITY FUNCTIONS; VECTOR SPACES;

VECTOR QUANTIZATION;

EID: 37249001271     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72523-7_20     Document Type: Conference Paper

References (15)

1. I. Cohen, F. Cozman, N. Sebe, M. Cirelo, and T. Huang. Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction. IEEE Trans. Pattern Analysis Machine Intell., 26(12): 1553-1567, 2004.
2. L. Didaci, G. Giacinto, F. Roli, and G.L. Marcialis. A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition, 38(11):2188-2191, 2005.
3. R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. Wiley., 2001.
4. R.P.W. Duin. The combining classifier: To train or not to train? In International Conference on Pattern Recognition, volume II, pages 765-770, Canada, 2002.
5. S. Hettich, C.L. Blake, and C.J. Merz. UCI repository of ML databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
6. G. Hinton, M. Revow, and P. Dayan. Recognizing handwritten digits using mixtures of linear models. In Neural Inf. Proc. Syst., pages 1015-1022, 1995.
7. T.S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In Neural Inf. Proc. Syst., 1999.
8. M. Kambhatla and T.K. Leen. Dimension reduction by local principal component analysis. Neural Computation, 9:443-482, 1997.
9. C. Lai, D.M.J. Tax, R.P.W. Duin, E. Pekalska, and P. Paclík. A study on combining image representations for image classification and retrieval. International Journal of Pattern Recognition and Artificial Intelligence, 18(5):867-890, 2004.
10. M. Layton and M. Gales. Augmented statistical models: Exploiting generative models in discriminative classifiers. In Neural Inf. Proc. Syst., 2005.
11. G. McLachlan and D. Peel. Finite Mixture Models. John Wiley & Sons, Inc., 2000.
12. J.E. Moody and C. Daxken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:281-294, 1989.
13. A. Ng and M. Jordan. On discriminative vs generative classifiers: A comparison of logistic regression and naive Bayes. In Neural Inf. Proc. Syst., 2002.
14. M.E. Tipping and C. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11:443-482, 1999.
15. K. Woods, W.P. Kegelmeyer, and K. Bower. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on Pattern Recognition and Machine Intelligence, 19(4):405-409, 1997.