Margin-based active learning for LVQ networks

Neurocomputing

Volumn 70, Issue 7-9, 2007, Pages 1215-1224

  Schleif, F M ^a   Hammer, B ^b   Villmann, T ^a  

^a UNIVERSITY OF LEIPZIG   (Germany)

^b CLAUSTHAL UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Active learning; Classification; Generalization; Learning vector quantization; Proteomic profiling

Indexed keywords

COST FUNCTIONS; IMAGE CLASSIFICATION; NEURAL NETWORKS; QUERY PROCESSING; RANDOM PROCESSES; VECTOR QUANTIZATION;

ACTIVE LEARNING; LEARNING VECTOR QUANTIZATION; NEURAL GAS; PROTEOMIC PROFILING;

LEARNING SYSTEMS;

BIOLOGICAL MARKER;

ARTICLE; CALCULATION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; MACHINE LEARNING; MASS SPECTROMETRY; PREDICTION; PRIORITY JOURNAL; PROTEOMICS;

EID: 33847401462     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.10.149     Document Type: Article

References (21)

1. Bartlett P., and Mendelsohn S. Rademacher and Gaussian complexities: risk bounds and structural results. J. Mach. Learn. Res. 3 (2002) 463-482
2. Baum E. Neural net algorithms that learn in polynomial time from examples and queries. IEEE Trans. Neural Networks 2 (1991) 5-19
3. Belue L.M., Bauer Jr. K.W., and Ruck D.W. Selecting optimal experiments for multiple output multilayer perceptrons. Neural Comput. 9 (1997) 161-183
4. Biehl M., Ghosh A., and Hammer B. Learning vector quantization: the dynamics of winner-takes-all algorithms. Neurocomputing 69 7-9 (2006) 660-670
5. C. Blake, C. Merz, UCI repository of machine learning databases, available at: 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉, 1998.
6. C. Cambell, N. Cristianini, A. Smola, Query learning with large margin classifiers, in: International Conference in Machine Learning, 2000, pp. 111-118.
7. K. Crammer, R. Gilad-Bachrach, A. Navot, A. Tishby, Margin analysis of the LVQ algorithm, in: Proceedings of NIPS 2002, 〈http://www-.cs.cmu.edu/Groups/NIPS/NIPS2002/NIPS2002preproceedings/index.html〉, 2002.
8. Freund Y., Seung H.S., Shamir E., and Tishby N. Information, prediction and query by committee. Advances in Neural Information Processing Systems 1993 (1993) 483-490
9. Hammer B., Strickert M., and Villmann T. Supervised neural gas with general similarity measure. Neural Process. Lett. 21 1 (2005) 21-44
10. Hammer B., Strickert M., and Villmann T. On the generalization ability of GRLVQ networks. Neural Process. Lett. 21 2 (2005) 109-120
11. Hammer B., and Villmann T. Generalized relevance learning vector quantization. Neural Networks 15 8-9 (2002) 1059-1068
12. Hasenjäger M., and Ritter H. Active learning with local models. Neural Process. Lett. 7 (1998) 107-117
13. T. Kohonen, Self-Organizing Maps, Springer Series in Information Sciences, vol. 30, Springer, Berlin, Heidelberg, 1995 (2nd Ext. Ed. 1997).
14. Mitra P., Murthy C., and Pal S. A probabilistic active support vector learning algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 28 3 (2004) 412-418
15. Opper M. Statistical mechanics of generalization. In: Arbib M. (Ed). The Handbook of Brain Theory and Neural Networks (2003), MIT, Cambridge, MA 1087-1090
16. Sato A., and Yamada K. A formulation of learning vector quantization using a new misclassification measure. In: Jain A.K., Venkatesh S., and Lovell B.C. (Eds). Proceedings of the 14th International Conference on Pattern Recognition vol. 1 (1998), IEEE Computer Society, Los Alamitos, CA, USA 322-325
17. E. Schäffeler, U. Zanger, M. Schwab, M. Eichelbaum, Magnetic bead based human plasma profiling discriminate acute lymphatic leukaemia from non-diseased samples, in: 52nd ASMS Conference (ASMS) 2004, 2004, p. TPV 420.
18. F.-M. Schleif, T. Villmann, B. Hammer, Local metric adaptation for soft nearest prototype classification to classify proteomic data, in: Fuzzy Logic and Applications: Sixth International Workshop, WILF 2005, Lecture Notes in Computer Science (LNCS), Springer, Berlin, 2006, pp. 290-296.
19. Seo S., Bode M., and Obermayer K. Soft nearest prototype classification. IEEE Trans. Neural Networks 14 (2003) 390-398
20. Shawe-Taylor J., Bartlett P., Willimason R., and Anthony M. Structural risk minimization over data-dependent hierarchies. IEEE Trans. Inform. Theory 44 5 (1998) 1926-1940
21. Vapnik V. Statistical Learning Theory (1998), Wiley, New York