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Author

Weibo Liang

Bio: Weibo Liang is an academic researcher. The author has contributed to research in topics: Support vector machine & Particle swarm optimization. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Proceedings ArticleDOI
28 Dec 2009
TL;DR: A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm and Support Vector Machines, and these parameters are used to develop classification rules and train SVM.
Abstract: A hybrid intelligent system is applied to recognizing the investment risk of project, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.

1 citations


Cited by
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Journal Article
TL;DR: In this paper, the first step to develop the investment risk early warning system of infrastructure project is to design the investmentrisk early-warning index and then, using analytic hierarchy process (APH), the weights of investment risk index at infrastructure project are determined quantificationally.
Abstract: The first step to develop the investment risk early-warning system of infrastructure project is to design the investment risk early-warning index This paper establishes the indicator system of investment risk at infrastructure project Then, using analytic hierarchy process (APH), the weights of investment risk early-warning index at infrastructure project are determined quantificationally, which can be used to determine the investment risk early-warning index

1 citations