What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Data mining, Artificial intelligence, Machine learning, Intrusion detection system and Data stream mining.
In his research, he undertakes multidisciplinary study on Data mining and Credit card fraud.
Wei Fan has researched Artificial intelligence in several fields, including Weighting, Graph and Pattern recognition.
His Machine learning study frequently links to related topics such as Classifier.
His work carried out in the field of Intrusion detection system brings together such families of science as Security policy, Anomaly detection and Computer network, Mobile computing.
His research integrates issues of Data stream and Training set in his study of Data stream mining.
His most cited work include:
- Mining concept-drifting data streams using ensemble classifiers (1075 citations)
- Method and system for using intelligent agents for financial transactions, services, accounting, and advice (554 citations)
- Mining big data: current status, and forecast to the future (521 citations)
What are the main themes of his work throughout his whole career to date?
Wei Fan spends much of his time researching Artificial intelligence, Data mining, Machine learning, Pattern recognition and Information retrieval.
His studies in Artificial intelligence integrate themes in fields like Graph, Computer vision and Natural language processing.
The Data mining study combines topics in areas such as Scalability and Cluster analysis.
His studies in Semi-supervised learning, Decision tree, Unsupervised learning, Boosting and Transfer of learning are all subfields of Machine learning research.
His research ties Feature and Pattern recognition together.
Many of his studies on Data stream mining involve topics that are commonly interrelated, such as Data stream.
He most often published in these fields:
- Artificial intelligence (46.31%)
- Data mining (29.79%)
- Machine learning (25.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (46.31%)
- Pattern recognition (11.21%)
- Information retrieval (10.03%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Information retrieval, Natural language processing and Computer vision.
As part of his studies on Artificial intelligence, Wei Fan frequently links adjacent subjects like Machine learning.
His study explores the link between Machine learning and topics such as Reduction that cross with problems in Class.
His research in Pattern recognition intersects with topics in Convolution and Code.
His studies examine the connections between Information retrieval and genetics, as well as such issues in Key, with regards to Annotation.
His Natural language processing research integrates issues from Classifier and Graph.
Between 2018 and 2021, his most popular works were:
- AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (141 citations)
- Joint Slot Filling and Intent Detection via Capsule Neural Networks (70 citations)
- Multi-grained Named Entity Recognition. (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Wei Fan mostly deals with Artificial intelligence, Information retrieval, Artificial neural network, Question answering and Pattern recognition.
His work deals with themes such as Computer vision and Natural language processing, which intersect with Artificial intelligence.
The concepts of his Information retrieval study are interwoven with issues in Ranking, Key and Knowledge base.
The various areas that he examines in his Artificial neural network study include Psoriasis, Utterance, Data mining and Medical record.
His Question answering research includes themes of Transfer of learning, Closed captioning, Knowledge extraction and Federated learning.
His Deep learning study improves the overall literature in Machine learning.
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