论文信息 - Tree-shaped Decision Schema for Multi-classification

Tree-shaped Decision Schema for Multi-classification

This paper presents a novel M-class classification schema. SVCMR creates a tree-shaped decision frame which contains M/2 three-separation classifiers as decision nodes. In an informed order, a series of basic classifiers are trained on the reduced dataset, and this order ensures the less number of basic models to cover decisions on all classes. SVCMR is characterized with individual parameterization of Kernel scale and penalty coefficient. Experiments on real experiments demonstrate the fine performance of SVCMR.

[1] Daniel Boley,et al. Training Support Vector Machines Using Adaptive Clustering , 2004, SDM.

[2] Jason Weston,et al. Multi-Class Support Vector Machines , 1998 .

[3] Hava T. Siegelmann,et al. Support Vector Clustering , 2002, J. Mach. Learn. Res..

[4] Dustin Boswell,et al. Introduction to Support Vector Machines , 2002 .

[5] Nello Cristianini,et al. Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[6] Harris Drucker,et al. Comparison of learning algorithms for handwritten digit recognition , 1995 .

[7] Fernando Pérez-Cruz,et al. Multi-class support vector machines: a new approach , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[8] Masao Fukushima,et al. A new multi-class support vector algorithm , 2006, Optim. Methods Softw..

[9] Koby Crammer,et al. On the Learnability and Design of Output Codes for Multiclass Problems , 2002, Machine Learning.

[10] E Comak,et al. A support vector machine using the lazy learning approach for multi-class classification , 2006, Journal of medical engineering & technology.

[11] Andreu Català,et al. K-SVCR. A support vector machine for multi-class classification , 2003, Neurocomputing.

[12] Robert Tibshirani,et al. Classification by Pairwise Coupling , 1997, NIPS.

[13] Vladimir Vapnik,et al. Statistical learning theory , 1998 .