Improving Svm learning accuracy with adaboost

Proceedings - 4th International Conference on Natural Computation, ICNC 2008

Volumn 3, Issue , 2008, Pages 221-225

  Zhang, Xiaolong ^a   Ren, Fang ^a  

^a WUHAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Adaboost algorithm; Boosting algorithm; SVM

Indexed keywords

EDUCATION; LEARNING ALGORITHMS; OPTIMIZATION;

ADABOOST; ADABOOST ALGORITHM; BOOSTING ALGORITHM; COMPETITIVE LEARNINGS; DO-MAINS; LEARNING ACCURACIES; LEARNING PERFORMANCES; RE SAMPLINGS; STRUCTURAL RISK MINIMIZATIONS; SUPPORT VECTORS; SVM; SVM ALGORITHMS; TRAINING EXAMPLES;

SUPPORT VECTOR MACHINES;

EID: 57649131019     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2008.841     Document Type: Conference Paper

References (11)

1. Vapnik V. N., "The Nature of Statistical Learning Theory", Springer-Verlag, New York, 1995.
2. Vapnik V. N., "Principles of Risk Minimization for Learning Theory", Advances in Neural Information Processing Systems 4, 831-838, San Mateo: Morgan Kaufmann, 1992.
3. Dong J. X., Krzyzak A., Suen C. Y., "Fast SVM Training Algorithm with Decomposition on Very Large Data Sets", PAMI(27), No.4, 603-618, April 2005.
4. Ramirez L., Durdle N. G., Raso V. J., Hill D. L., "A Support Vector Machines Classifier to Assess the Severity of Idiopathic Scoliosis from Surface Topography", IEEE Transactions on Information Technology in Biomedicine, Vol.10, p.841, 2006.
5. Freund Y. and Schapire R. E., "A Decision-theoretical Generalization of On-line Learning and an Application to Boosting", Journal of Computer and System Sciences, 55(1):119-139, August 1997.
6. Wang X. and Sun D., "A Combined SVM Classifier Based on AdaBoost", Journal of Air Force Engineering University, 2006.
7. Zhang X. and Lin L., "Toward Optimization of SVM Learning with RBF Kernel", ITC-CSCC 2006 Thai, July 2006.
8. Richard O.Duda, Peter E.Hart and David G.Stork., Pattern Classification, Second Edition, 2001.
9. Chang C. and Lin C., "LIBSVM-A Library for Support Vector Machines", [http://www.csie.ntu.edu.tw/cjlin/libsvm/index.html].
10. Chen P. H., Fan R. E. and Lin C. J., "A Study on SMO-Type Decomposition Methods for Support Vector Machine", IEEE Transactions on Neural Networks, 17:893-908, July 2006.
11. Asuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository [www.ics.uoi.edu/~inlearn/MLRepository.html].