The scientist’s investigation covers issues in Artificial intelligence, Support vector machine, Extreme learning machine, Machine learning and Artificial neural network. His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition. His studies in Support vector machine integrate themes in fields like Kernel, Particle swarm optimization, Variables, Simulation and Least squares.
His biological study spans a wide range of topics, including Real-time computing, Kernel principal component analysis and Interrupt. His work in the fields of Boltzmann machine, Restricted Boltzmann machine and Deep belief network overlaps with other areas such as Special case. His Deep learning research integrates issues from Feature extraction, Discriminative model and Pooling.
His primary areas of investigation include Artificial intelligence, Extreme learning machine, Support vector machine, Pattern recognition and Machine learning. In most of his Artificial intelligence studies, his work intersects topics such as Data mining. His work carried out in the field of Extreme learning machine brings together such families of science as Algorithm, Overfitting, Adaptive control and Kernel.
His Support vector machine study combines topics from a wide range of disciplines, such as Torque, Automotive engineering, Automotive engine and Kernel. In general Pattern recognition study, his work on Feature learning and Unsupervised learning often relates to the realm of Spatial analysis, thereby connecting several areas of interest. His research in Machine learning intersects with topics in Class and Benchmark.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Extreme learning machine, Artificial neural network and Deep learning. He has included themes like Machine learning and Computer vision in his Artificial intelligence study. His research integrates issues of Gradient descent, Data stream mining, Concept drift and Thresholding in his study of Pattern recognition.
His Extreme learning machine research includes themes of PID controller, Automotive engine, Kernel and Pattern recognition. His Artificial neural network study integrates concerns from other disciplines, such as Decimation, Word and Computation. In his study, which falls under the umbrella issue of Deep learning, 3d shapes is strongly linked to Pooling.
Chi-Man Vong spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Pooling and Feature learning. His Artificial intelligence research is multidisciplinary, relying on both Ranking and Data stream mining. Chi-Man Vong combines subjects such as Gradient descent, 3d shapes, Thresholding and Concept drift with his study of Pattern recognition.
He has researched Deep learning in several fields, including Voxel and Shape analysis. His Pooling research is multidisciplinary, relying on both Machine learning and Discriminative model. His research integrates issues of Prognostics, Feature extraction and Support vector machine in his study of Feature learning.
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Extreme Learning Machine
Erik Cambria;Guang-Bin Huang;Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou.
Representational learning with ELMs for big data
Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou;Guang-Bin Huang;Chi Man Vong.
IEEE Intelligent Systems (2013)
Local Receptive Fields Based Extreme Learning Machine
Guang-Bin Huang;Zuo Bai;Liyanaarachchi Lekamalage Chamara Kasun;Chi Man Vong.
IEEE Computational Intelligence Magazine (2015)
Sparse Bayesian Extreme Learning Machine for Multi-classification
Jiahua Luo;Chi-Man Vong;Pak-Kin Wong.
IEEE Transactions on Neural Networks (2014)
Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference
Chi-Man Vong;Pak-Kin Wong;Yi-Ping Li.
Engineering Applications of Artificial Intelligence (2006)
Rate-Dependent Hysteresis Modeling and Control of a Piezostage Using Online Support Vector Machine and Relevance Vector Machine
Pak-Kin Wong;Qingsong Xu;Chi-Man Vong;Hang-Cheong Wong.
IEEE Transactions on Industrial Electronics (2012)
Kernel-Based Multilayer Extreme Learning Machines for Representation Learning
Chi Man Wong;Chi Man Vong;Pak Kin Wong;Jiuwen Cao.
IEEE Transactions on Neural Networks (2018)
Real-time fault diagnosis for gas turbine generator systems using extreme learning machine
Pak Kin Wong;Zhixin Yang;Chi Man Vong;Jianhua Zhong.
Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search
Pak Kin Wong;Ka In Wong;Chi Man Vong;Chun Shun Cheung.
Renewable Energy (2015)
SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention
Zhizhong Han;Mingyang Shang;Zhenbao Liu;Chi-Man Vong.
IEEE Transactions on Image Processing (2019)
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