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Chew, H.-G., Crisp, D. J., Bogner, R. E. et al. (2000). Target Detection in Radar Imagery Using Support Vector Machines with Training Size Biasing. In Proceedings of the Sixth International Conference on Control, Automation, Robotics and Vision, Singapore.

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• TITLE: The P2P Risk Assessment Model Based on the Improved AdaBoost-SVM Algorithm

  AUTHORS: Jianhui Yang, Dongsheng Luo

  KEYWORDS: Peers-to-Peers, AdaBoost, SVM, The Combinations of Learning Machine, Rule Sampling

  JOURNAL NAME: Journal of Financial Risk Management, Vol.6 No.2, June 27, 2017

  ABSTRACT: The improved AdaBoost-SVM algorithm is used to classify the safety and the risk from the Peers-to-Peers net loan platforms. Since the SVM algorithm is hard to deal with the rare samples and its training is slow, rule sampling is used to reduce the classify noise. Then, with the combinations of learning machine, P2P risks can be identified. The result shows that IAdaBoost algorithm can improve the risk platform classification accuracy. And the error of classification can be controlled in 5%.