Decentralized Machine Learning Intelligence > Topics > SVM Aggregating Intelligence

SVM Aggregating Intelligence


SVM Aggregating Intelligence is a set of methodologies and approaches to collect computing intellect of multiple SVMs through construction of SVM based multi-core complex system to which single SVM methodologies and approaches are ineffective or infeasible. In this field, we developed early several SVM modular aggregations, such as random SVM mixture expert system, SVM Ensemble; ordered SVM aggregation system, SVM Classification Trees (SVMT); SVMT association rule mining system rSVMT, and personalized transductive learning SVMT, ptSVMT.



Related articles:

  • S. Pang, D. Kim and S.Y. Bang, "Face membership authentication using SVM classification tree generated by membership-based LLE data partition," IEEE Transactions on Neural Networks, vol. 16, no. 2, pp. 436 -446, 2005.|PDF|Bibtxt|
  • S. Pang, "Constructing SVM Multiple Tree for Face Membership Authentication," Biometric Authentication, vol. 3072, pp. 1-13, Springer Berlin / Heidelberg, 2004.|PDF|Bibtxt|
  • S. Pang, D. Kim and S.Y. Bang, "Membership authentication in the dynamic group by face classification using SVM ensemble," Pattern Recognition Letters, vol. 24, no. 1-3, pp. 215 - 225, 2003.|PDF|Bibtxt|
  • H. C. Kim, S. Pang, H. M. Je, D. Kim and S. Y. Bang, "Constructing support vector machine ensemble," Pattern Recognition, vol. 36, no. 12, pp. 2757 - 2767, 2003.|PDF|Bibtxt|
  • S. Pang, T. Ban, Y. Kadobayashi and N. Kasabov, "Personalized mode transductive spanning SVM classification tree," Information Sciences, vol. 181, no. 11, pp. 2071 - 2085, 2011.|PDF|Bibtxt|
  • S. Pang and N. Kasabov, "Encoding and decoding the knowledge of association rules over SVM classification trees," Knowledge and Information Systems, vol. 19, no. 1, pp. 79-105, 2009.|PDF|Bibtxt|