What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
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 most cited work include:
- Get another label? improving data quality and data mining using multiple, noisy labelers (800 citations)
- Incremental Support Vector Learning for Ordinal Regression (611 citations)
- A Robust Regularization Path Algorithm for $
u $ -Support Vector Classification (401 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (57.14%)
- Machine learning (37.93%)
- Data mining (29.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (57.14%)
- Pattern recognition (23.15%)
- Machine learning (37.93%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- A convolutional neural network-based linguistic steganalysis for synonym substitution steganography. (26 citations)
- Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering (13 citations)
- Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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.
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