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Faculty of Engineering and Information Sciences - Papers: Part A

2014

Training

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A Sparsity-Based Training Algorithm For Least Squares Svm, Jie Yang, Jun Ma Jan 2014

A Sparsity-Based Training Algorithm For Least Squares Svm, Jie Yang, Jun Ma

Faculty of Engineering and Information Sciences - Papers: Part A

We address the training problem of the sparse Least Squares Support Vector Machines (SVM) using compressed sensing. The proposed algorithm regards the support vectors as a dictionary and selects the important ones that minimize the residual output error iteratively. A measurement matrix is also introduced to reduce the computational cost. The main advantage is that the proposed algorithm performs model training and support vector selection simultaneously. The performance of the proposed algorithm is tested with several benchmark classification problems in terms of number of selected support vectors and size of the measurement matrix. Simulation results show that the proposed algorithm …


Training Laboratory: Using Online Resources To Enhance The Laboratory Learning Experience, Sasha Nikolic Jan 2014

Training Laboratory: Using Online Resources To Enhance The Laboratory Learning Experience, Sasha Nikolic

Faculty of Engineering and Information Sciences - Papers: Part A

2014 IEEE. Technology has enabled students to search and utilize information from a diverse range of sources. One mechanism that students turn to for additional resources is the internet. This paper explores student interaction with an internet resource, called the Training Laboratory. This resource has multiple uses, including: 1) the training of laboratory teaching assistants; 2) providing students an opportunity to develop pre-requisite laboratory skills; 3) reduce the workload of developing resources when designing laboratory notes; 4) reduce the duplication of learning fundamental laboratory skills in multiple subjects; 5) provide a means to share resources to satellite campuses; and, 6) …