2023 - Research.com Computer Science in China Leader Award
2018 - IEEE Fellow For contributions to semi-supervised learning for signal processing
Changshui Zhang mostly deals with Artificial intelligence, Pattern recognition, Machine learning, Cluster analysis and Dimensionality reduction. The concepts of his Artificial intelligence study are interwoven with issues in Graph and Computer vision. His studies link Data point with Pattern recognition.
His work on Feature selection as part of general Machine learning research is frequently linked to Network traffic simulation, Traffic flow and Data flow diagram, bridging the gap between disciplines. His Cluster analysis research is multidisciplinary, incorporating perspectives in Matrix decomposition, Theoretical computer science and Data mining. His Dimensionality reduction study incorporates themes from Embedding, Subspace topology, Linear discriminant analysis and Curse of dimensionality.
His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Algorithm and Computer vision. His work is dedicated to discovering how Artificial intelligence, Data mining are connected with Mixture model and other disciplines. Changshui Zhang regularly links together related areas like Facial recognition system in his Pattern recognition studies.
His work in Machine learning addresses issues such as Image retrieval, which are connected to fields such as Active learning. His work on Mathematical optimization expands to the thematically related Algorithm. His Semi-supervised learning research incorporates themes from Unsupervised learning and Graph.
Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Artificial neural network are his primary areas of study. His study in Robustness, Adversarial system, Convolutional neural network, MNIST database and Recurrent neural network is done as part of Artificial intelligence. In his study, which falls under the umbrella issue of Machine learning, Neural coding is strongly linked to Code.
Changshui Zhang focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Image and, in some cases, Interpolation and Degradation. His research integrates issues of Object and Data mining in his study of Deep learning. His Artificial neural network research includes themes of Network architecture, Convolution and Pruning.
His primary areas of investigation include Artificial intelligence, Machine learning, Robustness, Algorithm and Artificial neural network. His Artificial intelligence study combines topics in areas such as Code and Pattern recognition. As a part of the same scientific family, Changshui Zhang mostly works in the field of Pattern recognition, focusing on Sequence learning and, on occasion, Recurrent neural network, Gesture recognition, Optical flow and Feature extraction.
His studies deal with areas such as Training set, Linear subspace and Benchmark as well as Machine learning. His work focuses on many connections between Robustness and other disciplines, such as Adversarial system, that overlap with his field of interest in Computation and Optimization problem. His Algorithm research is multidisciplinary, relying on both Network architecture, Convolution and Pruning.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Learning Efficient Convolutional Networks through Network Slimming
Zhuang Liu;Jianguo Li;Zhiqiang Shen;Gao Huang.
international conference on computer vision (2017)
Label Propagation through Linear Neighborhoods
Fei Wang;Changshui Zhang.
IEEE Transactions on Knowledge and Data Engineering (2008)
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs
Zhonglin Lin;Changshui Zhang;Wei Wu;Xiaorong Gao.
IEEE Transactions on Biomedical Engineering (2006)
A bayesian network approach to traffic flow forecasting
Shiliang Sun;Changshui Zhang;Guoqiang Yu.
IEEE Transactions on Intelligent Transportation Systems (2006)
Learning a Mahalanobis distance metric for data clustering and classification
Shiming Xiang;Feiping Nie;Changshui Zhang.
Pattern Recognition (2008)
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
Feiping Nie;Dong Xu;Ivor Wai-Hung Tsang;Changshui Zhang.
IEEE Transactions on Image Processing (2010)
A two-step approach to hallucinating faces: global parametric model and local nonparametric model
Ce Liu;Heung-Yeung Shum;Chang-Shui Zhang.
computer vision and pattern recognition (2001)
Manifold-ranking based image retrieval
Jingrui He;Mingjing Li;Hong-Jiang Zhang;Hanghang Tong.
acm multimedia (2004)
Blur detection for digital images using wavelet transform
Hanghang Tong;Mingjing Li;Hongjiang Zhang;Changshui Zhang.
international conference on multimedia and expo (2004)
Trace ratio criterion for feature selection
Feiping Nie;Shiming Xiang;Yangqing Jia;Changshui Zhang.
national conference on artificial intelligence (2008)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: