A self-organizing neural network for detecting novelties

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2007, Pages 462-466

  Albertini, Marcelo Keese ^a   De Mello, Rodrigo Fernandes ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Novelty detection; Self organizing neural networks

Indexed keywords

DATA ACQUISITION; LEARNING SYSTEMS; NEURONS; ONLINE SYSTEMS; PARAMETER ESTIMATION; PATTERN RECOGNITION;

NOVELTY EVENTS; PATTERN DYNAMIC CHARACTERISTICS; SELF-ORGANIZING NEURAL NETWORK; SELF-ORGANIZING NOVELTY DETECTION (SONDE);

NEURAL NETWORKS;

EID: 35248885111     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1244002.1244110     Document Type: Conference Paper

References (12)

1. A. A. Alizadeh and et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, 403(6769):503-511, February 2000.
2. R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern, Information Retrieval. ACM Press / Addison-Wesley, 1999.
3. G. A. Carpenter, S. Grossberg, and D. B. Rosen. ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition. Neural Networks, 4:4934-504, 1991.
4. L. Cox, M. Johnson, and K. Kafadar. Exposition of statistical graphics technology. In ASA Proceedings Statistical Computation Section, pages 55-56, 1982.
5. A. Flexer, E. Pampalk, and G. Widmer. Novelty detection based on spectral similarity of songs. In Proceedings of 6th International Conference on Music Information Retrieval, pages 260-263, September 2005.
6. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439):531-537, October 1999.
7. M. Markou and S. Singh. Novelty detection: a review,part i: statistical approaches. Signal Process, 83(12):2481-2497, 2003.
8. S. Marsland, J. Shapiro, and U. Nehmzow. A self-organising network that grows when required. Neural Networks, 15(8-9):1041-1058, October 2002.
9. E. J. Spinosa and A. C. de Carvalho. Combining one-class classifiers for robust novelty detection in gene expression data. Lecture Notes in Computer Science, 3594:54-64, August 2005.
10. L. Tarassenko. Novelty detection for the identification of masses in mammograms. In 4th IEE International Conference on Artificial Neural Networks, volume 4, pages 442-447, Cambridge, UK, 1995.
11. V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA, 1995.
12. A. Ypma and R. P. W. Duin. Novelty detection using self-organizing maps. In Progress in Connectionist-Based Information Systems, volume 2, pages 1322-1325. Springer, London, 1997.