S
Sung Yang Bang
Researcher at Pohang University of Science and Technology
Publications - 29
Citations - 1389
Sung Yang Bang is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Support vector machine & Mixture model. The author has an hindex of 17, co-authored 29 publications receiving 1312 citations.
Papers
More filters
Journal ArticleDOI
Constructing support vector machine ensemble
TL;DR: Simulation results for the IRIS data classification and the hand-written digit recognition, and the fraud detection show that the proposed SVM ensemble with bagging or boosting outperforms a single SVM in terms of classification accuracy greatly.
Book ChapterDOI
Support Vector Machine Ensemble with Bagging
TL;DR: Simulation results for the IRIS data classification and the hand-written digit recognitions show that the proposed SVM ensembles with bagging outperforms a single SVM in terms of classification accuracy greatly.
Journal ArticleDOI
Membership authentication in the dynamic group by face classification using SVM ensemble
TL;DR: This paper presents a method for authenticating an individual's membership in a dynamic group without revealing the individual's identity and without restricting the group size and/or the members of the group.
Journal ArticleDOI
Face membership authentication using SVM classification tree generated by membership-based LLE data partition
TL;DR: The experimental results show that the proposed SVM tree not only keeps the good properties that the SVM ensemble method has, such as a good authentication accuracy and the robustness to the change of members, but also has a considerable improvement on the stability under the changes of membership group size.
Journal ArticleDOI
Appearance-based gender classification with Gaussian processes
TL;DR: Gaussian process classifiers (GPCs) are proposed which are Bayesian kernel classifiers which determine the hyperparameters of the kernel based on Bayesian model selection criterion and can be used to improve SVM performance.