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A new boosting design of Support Vector Machine classifiers
(Elsevier, 2015)
Boosting algorithms pay attention to the particular structure of the training data when learning, by means of iteratively emphasizing the importance of the training samples according to their difficulty for being correctly ...
Management and entrepreneurship management mechanism of college students based on support vector machine algorithm
For the employment and entrepreneurship management of college students, the application of big data technology can effectively improve their work efficiency, that is, the support vector machine algorithm is applied to the ...
Interior Proximal Algorithm with Variable Metric for Second-Order Cone Programming: Applications to Structural Optimization and Support Vector Machines
(Taylor & Francis, 2009-10)
In this work, we propose an inexact interior proximal-type algorithm for solving convex second-order cone programs. This kind of problem consists of minimizing a convex function (possibly nonsmooth) over the intersection ...
Supporting academic decision making at higher educational institutions using machine learning-based algorithms
Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables ...
Precipitates segmentation from scanning electron microscope images through machine learning techniques
(2011-06-02)
The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable ...
A Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning
(Ieee, 2014-01-01)
The choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we ...