What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Qiang Yang mainly focuses on Artificial intelligence, Machine learning, Data mining, Transfer of learning and Pattern recognition.
His Artificial intelligence research integrates issues from Domain and Multi-task learning, Task.
His study in Multi-task learning is interdisciplinary in nature, drawing from both Active learning, Instance-based learning and Feature.
His study in Collaborative filtering, Support vector machine, Unsupervised learning, Labeled data and Decision tree is carried out as part of his Machine learning studies.
His research on Data mining often connects related topics like Regression analysis.
His Transfer of learning research is multidisciplinary, incorporating perspectives in Contextual image classification, Knowledge transfer and Feature vector.
His most cited work include:
- A Survey on Transfer Learning (9736 citations)
- Top 10 algorithms in data mining (3313 citations)
- Graph Embedding and Extensions: A General Framework for Dimensionality Reduction (2213 citations)
What are the main themes of his work throughout his whole career to date?
Qiang Yang mainly investigates Artificial intelligence, Machine learning, Data mining, Transfer of learning and Information retrieval.
His research integrates issues of Domain, Task and Pattern recognition in his study of Artificial intelligence.
His work deals with themes such as Multi-task learning and Knowledge transfer, which intersect with Machine learning.
Transfer of learning is frequently linked to Recommender system in his study.
He most often published in these fields:
- Artificial intelligence (32.46%)
- Machine learning (20.02%)
- Data mining (14.97%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (32.46%)
- Machine learning (20.02%)
- Transfer of learning (9.08%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Transfer of learning, Photovoltaic system and Deep learning.
Qiang Yang interconnects Domain and Pattern recognition in the investigation of issues within Artificial intelligence.
The various areas that he examines in his Machine learning study include Task and Benchmark.
His Transfer of learning research is multidisciplinary, incorporating elements of Recommender system and Representation.
He has researched Photovoltaic system in several fields, including Real-time computing and Mathematical optimization.
His study looks at the relationship between Mathematical optimization and fields such as Distributed generation, as well as how they intersect with chemical problems.
Between 2017 and 2021, his most popular works were:
- Federated Machine Learning: Concept and Applications (800 citations)
- Advances and Open Problems in Federated Learning (571 citations)
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey (190 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Machine learning, Transfer of learning, Deep learning and Domain are his primary areas of study.
The concepts of his Artificial intelligence study are interwoven with issues in Task and Pattern recognition.
His work carried out in the field of Machine learning brings together such families of science as Representation, Knowledge transfer and Benchmark.
His Transfer of learning research incorporates elements of Recommender system, Feature learning and Service.
His studies in Deep learning integrate themes in fields like Natural language processing, Edge computing, Data mining and Graph.
He combines subjects such as Sentiment analysis and Transfer with his study of Domain.
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