论文信息 - Combining classifiers based on confidence values

Combining classifiers based on confidence values

The paper describes our investigation into the neural gas (NG) network algorithm and the hierarchical overlapped architecture (HONG) which we have built by retaining the essence of the original NG algorithm. By defining an implicit ranking scheme, the NG algorithm was made to run faster in its sequential implementation. Each HONG network generated multiple classifications for every sample data presented as confidence values. These confidence values were combined to obtain the final classification of the HONG architecture. Three HONG networks based on three different feature sets with global and structural features were also trained to obtain better classification on conflicting handwritten data. An excellent recognition rate for the NIST SD3 database was consequently obtained.

Ponnuthurai N. Suganthan | Ajantha S. Atukorale | P. Suganthan | Ajantha S Atukorale

[1] Horst Bunke,et al. Off-Line, Handwritten Numeral Recognition by Perturbation Method , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2] Wan-Chi Siu,et al. Fast sequential implementation of "neural-gas" network for vector quantization , 1998, IEEE Trans. Commun..

[3] Thomas Martinetz,et al. 'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[4] Teuvo Kohonen,et al. The self-organizing map , 1990 .

[5] Galina L. Rogova,et al. Combining the results of several neural network classifiers , 1994, Neural Networks.

[6] Ponnuthurai N. Suganthan. Hierarchical overlapped SOM's for pattern classification , 1999, IEEE Trans. Neural Networks.

[7] Sandro Ridella,et al. On the importance of sorting in "neural gas" training of vector quantizers , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).