His primary scientific interests are in Artificial intelligence, Machine learning, Set, Data mining and Support vector machine. His research integrates issues of Computer vision and Pattern recognition in his study of Artificial intelligence. His research in Machine learning intersects with topics in Crowdsourcing and Key.
Victor S. Sheng integrates several fields in his works, including Set and Data quality. His Data mining study integrates concerns from other disciplines, such as Value, Control and Recurrent neural network. Victor S. Sheng interconnects Algorithm design, Mathematical optimization, Finite set and Benchmark in the investigation of issues within Support vector machine.
His main research concerns Artificial intelligence, Machine learning, Data mining, Pattern recognition and Crowdsourcing. Victor S. Sheng integrates Artificial intelligence and Data quality in his research. His work on Active learning and Supervised learning as part of general Machine learning study is frequently linked to Set, bridging the gap between disciplines.
His work on Relation as part of general Data mining study is frequently linked to Road networks, therefore connecting diverse disciplines of science. The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network and Feature. In his work, Ground truth is strongly intertwined with Inference, which is a subfield of Crowdsourcing.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Recommender system and Recurrent neural network. His work on Sparse matrix expands to the thematically related Artificial intelligence. His study in Pattern recognition is interdisciplinary in nature, drawing from both Multi target and Feature.
His work deals with themes such as Crowdsourcing, Construct, Representation and Inference, which intersect with Machine learning. His research integrates issues of Web service and Natural language processing in his study of Recommender system. The study incorporates disciplines such as Context, Speech recognition, Convolutional neural network and Measure in addition to Recurrent neural network.
Victor S. Sheng spends much of his time researching Artificial intelligence, Deep learning, Recurrent neural network, Recommender system and Task analysis. The Pattern recognition research he does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Satellite, therefore creating a link between diverse domains of science. His research on Artificial neural network and Machine learning is centered around Recurrent neural network.
Victor S. Sheng has included themes like Sequence, Representation, Layer and Flexibility in his Machine learning study. His Recommender system research incorporates themes from Quality of service and Web service. His Lesion segmentation study in the realm of Pattern recognition connects with subjects such as Cascade.
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.
Get another label? improving data quality and data mining using multiple, noisy labelers
Victor S. Sheng;Foster Provost;Panagiotis G. Ipeirotis.
knowledge discovery and data mining (2008)
Incremental Support Vector Learning for Ordinal Regression
Bin Gu;Victor S. Sheng;Keng Yeow Tay;Walter Romano.
IEEE Transactions on Neural Networks (2015)
Incremental learning for ν -Support Vector Regression
Bin Gu;Victor S. Sheng;Zhijie Wang;Derek Ho.
Neural Networks (2015)
A Robust Regularization Path Algorithm for $ u $ -Support Vector Classification
Bin Gu;Victor S. Sheng.
IEEE Transactions on Neural Networks (2017)
Structural Minimax Probability Machine
Bin Gu;Xingming Sun;Victor S. Sheng.
IEEE Transactions on Neural Networks (2017)
A Comparative Study of SIFT and its Variants
Jian Wu;Zhiming Cui;Victor S. Sheng;Pengpeng Zhao.
Measurement Science Review (2013)
Repeated labeling using multiple noisy labelers
Panagiotis G. Ipeirotis;Foster Provost;Victor S. Sheng;Jing Wang.
Data Mining and Knowledge Discovery (2014)
Cost-Sensitive Learning and the Class Imbalance Problem
Charles X. Ling;Victor S. Sheng.
Test strategies for cost-sensitive decision trees
C.X. Ling;V.S. Sheng;Q. Yang.
IEEE Transactions on Knowledge and Data Engineering (2006)
Thresholding for making classifiers cost-sensitive
Victor S. Sheng;Charles X. Ling.
national conference on artificial intelligence (2006)
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