What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
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 most cited work include:
- Extreme Learning Machine (558 citations)
- Representational learning with ELMs for big data (532 citations)
- Local Receptive Fields Based Extreme Learning Machine (240 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (48.87%)
- Extreme learning machine (30.08%)
- Support vector machine (24.81%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (48.87%)
- Pattern recognition (22.56%)
- Extreme learning machine (30.08%)
In recent papers he was focusing on the following fields of study:
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.
Between 2018 and 2021, his most popular works were:
- SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention (76 citations)
- 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation (51 citations)
- Unsupervised Learning of 3-D Local Features From Raw Voxels Based on a Novel Permutation Voxelization Strategy (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
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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