What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Transfer of learning, Machine learning, Activity recognition and Extreme learning machine.
His research investigates the link between Artificial intelligence and topics such as Pattern recognition that cross with problems in Artificial neural network.
His work deals with themes such as Domain, Knowledge transfer, Global Positioning System and Data set, which intersect with Transfer of learning.
His Machine learning research includes elements of Class and Feature extraction.
Yiqiang Chen interconnects Majority rule, Ubiquitous computing, Human–computer interaction, Wearable technology and Exploit in the investigation of issues within Activity recognition.
His Extreme learning machine research incorporates elements of Semi-supervised learning, Active learning, Multiclass classification and Big data.
His most cited work include:
- Extreme Learning Machine (558 citations)
- Deep Learning for Sensor-based Activity Recognition: A Survey (508 citations)
- Weighted extreme learning machine for imbalance learning (436 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Activity recognition.
His study involves Extreme learning machine, Transfer of learning, Classifier, Deep learning and Artificial neural network, a branch of Artificial intelligence.
His research in Transfer of learning intersects with topics in Domain and Knowledge transfer.
His Machine learning study incorporates themes from Feature extraction and Data mining.
His research on Pattern recognition often connects related topics like Feature.
His research in Activity recognition intersects with topics in Ubiquitous computing and Wearable computer.
He most often published in these fields:
- Artificial intelligence (47.28%)
- Machine learning (16.61%)
- Pattern recognition (15.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (47.28%)
- Transfer of learning (8.31%)
- Machine learning (16.61%)
In recent papers he was focusing on the following fields of study:
Yiqiang Chen mostly deals with Artificial intelligence, Transfer of learning, Machine learning, Pattern recognition and Benchmark.
The study incorporates disciplines such as Domain and Computer vision in addition to Artificial intelligence.
His work carried out in the field of Transfer of learning brings together such families of science as Activity recognition and Adaptation.
His Activity recognition study integrates concerns from other disciplines, such as Knowledge transfer, Wearable computer and Human–computer interaction.
In general Machine learning, his work in Feature learning and Decision tree is often linked to Task linking many areas of study.
His work on Classifier as part of general Pattern recognition study is frequently linked to Surface, therefore connecting diverse disciplines of science.
Between 2018 and 2021, his most popular works were:
- FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare (54 citations)
- Easy Transfer Learning By Exploiting Intra-Domain Structures (30 citations)
- Transfer Learning with Dynamic Adversarial Adaptation Network (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary scientific interests are in Artificial intelligence, Transfer of learning, Machine learning, Activity recognition and Domain.
Many of his studies involve connections with topics such as Pattern recognition and Artificial intelligence.
The various areas that Yiqiang Chen examines in his Pattern recognition study include Channel, Recurrent neural network and Gesture, Gesture recognition.
His research in the fields of Feature learning overlaps with other disciplines such as Orchestration.
His research integrates issues of Knowledge transfer, Personalization, Wearable technology, Wearable computer and Human–computer interaction in his study of Activity recognition.
His Deep learning research integrates issues from Kernel method and Structural risk minimization.
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