论文信息 - Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way, aiming at understanding the influence of the number of prototypes and their assignment to classes. The theory of on-line learning allows a mathematical description of the learning dynamics in model situations. We demonstrate using a system of three prototypes the different behaviors of LVQ systems of multiple prototype and single prototype class representation.

Michael Biehl | Barbara Hammer | Aree Witoelar

[1] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.

[2] Michael Biehl,et al. On the dynamics of Vector Quantization and Neural Gas , 2007, ESANN.

[3] T Kohonen. The Self-Organising Map, a Possible Model of Brain Maps , 1997 .

[4] R. Urbanczik,et al. SELF-AVERAGING AND ON-LINE LEARNING , 1998, cond-mat/9805339.

[5] Canada Revenue Agency,et al. Self-employment income , 2004 .

[6] Michael Biehl,et al. Performance analysis of LVQ algorithms: A statistical physics approach , 2006, Neural Networks.

[7] T. Watkin,et al. THE STATISTICAL-MECHANICS OF LEARNING A RULE , 1993 .

[8] W. Altemeier,et al. Miscellaneous , 1848, Brain Research.

[9] Teuvo Kohonen,et al. Improved versions of learning vector quantization , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[10] Michael Biehl,et al. Dynamics of Multiple Prototype LVQ , 2006 .

[11] Teuvo Kohonen,et al. Learning vector quantization , 1998 .

[12] Michael Biehl,et al. A theoretical framework for analysing the dynamics of LVQ , 2004 .

[13] Pragya Agarwal,et al. Self-Organising Maps , 2008 .

[14] Michael Biehl,et al. Dynamics and Generalization Ability of LVQ Algorithms , 2007, J. Mach. Learn. Res..

[15] G. Rubin. Wages and salaries , 1980 .