S
Shaoning Pang
Researcher at Unitec Institute of Technology
Publications - 112
Citations - 2598
Shaoning Pang is an academic researcher from Unitec Institute of Technology. The author has contributed to research in topics: Support vector machine & Linear discriminant analysis. The author has an hindex of 19, co-authored 112 publications receiving 2264 citations. Previous affiliations of Shaoning Pang include Auckland University of Technology & Victoria University of Wellington.
Papers
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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.
Journal ArticleDOI
Incremental linear discriminant analysis for classification of data streams
TL;DR: The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.
Journal ArticleDOI
Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment
Lianyong Qi,Chunhua Hu,Xuyun Zhang,Mohammad Reza Khosravi,Suraj Sharma,Shaoning Pang,Tian Wang +6 more
TL;DR: A novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique and Experimental results show better prediction performances of the approach compared to other competitive ones.
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
Incremental Learning of Chunk Data for Online Pattern Classification Systems
TL;DR: A pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where training samples are presented only once, and it is suggested that chunk IPCA can reduce the training time effectively as compared with I PCA unless the number of input attributes is too large.