Shaoyi Zhang

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Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used to map the input space into a high dimensional feature space. However, it can perform rather poorly when there are too many dimensions (e.g. for gene expression data) or when there(More)
Updating the structural model of complex structures is time-consuming due to the large size of the finite element model (FEM). Using conventional methods for these cases is computationally expensive or even impossible. A two-level method, which combined the Kriging predictor and the component mode synthesis (CMS) technique, was proposed to ensure the(More)
Zhang, Tong. M.S., Purdue University, May 2011. GPU-Based Global Illumination using Lightcuts. Major Professor: James Mohler. Global Illumination aims to generate high quality images. But due to its high requirements, it is usually quite slow. Research documented in this thesis was intended to offer a hardware and software combined acceleration solution to(More)
Novelty detection based on cross validation was proposed to detect the damage of the cluster of medium and minor bridges under the effect of environmental factors. The effectiveness of this method was tested through the numerical example of one bridge group that was composed of five similar bridges. Meanwhile, advantages and disadvantages of this method(More)
Support vector machine (SVM) classifiers represent one of the most powerful and promising tools for solving classification problems. In the past decade SVMs have been shown to have excellent performance in the field of data mining. The standard SVM classifier treats all instances equally. However, in many applications we have different levels of confidence(More)
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