What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Organic chemistry
Qing He focuses on Artificial intelligence, Machine learning, Transfer of learning, Data mining and Supramolecular chemistry.
His Artificial intelligence study integrates concerns from other disciplines, such as Computation and Pattern recognition.
His work in Machine learning covers topics such as Generalization which are related to areas like Speedup.
His research integrates issues of Domain, Data modeling, Data science and Semi-supervised learning in his study of Transfer of learning.
His study in the fields of Apriori algorithm under the domain of Data mining overlaps with other disciplines such as A priori and a posteriori.
His Supramolecular chemistry study incorporates themes from Receptor, Primary and Artificial systems.
His most cited work include:
- Parallel K-Means Clustering Based on MapReduce (487 citations)
- Supervised representation learning: transfer learning with deep autoencoders (172 citations)
- Parallel extreme learning machine for regression based on MapReduce (127 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, Data mining, Pattern recognition and Computer vision.
His research is interdisciplinary, bridging the disciplines of Domain and Artificial intelligence.
His Machine learning study combines topics from a wide range of disciplines, such as Multi-task learning and Generalization.
His Data mining study incorporates themes from Process, Clustering high-dimensional data, Cluster analysis, Statistical classification and Speedup.
Qing He has included themes like Autoencoder and Deep learning in his Feature learning study.
His work deals with themes such as CURE data clustering algorithm and Data stream clustering, which intersect with Canopy clustering algorithm.
He most often published in these fields:
- Artificial intelligence (46.36%)
- Machine learning (24.78%)
- Data mining (16.62%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (46.36%)
- Machine learning (24.78%)
- Recommender system (4.96%)
In recent papers he was focusing on the following fields of study:
Qing He mostly deals with Artificial intelligence, Machine learning, Recommender system, Embedding and Data mining.
His Artificial intelligence research incorporates themes from Domain, Pattern recognition and Natural language processing.
Qing He focuses mostly in the field of Domain, narrowing it down to topics relating to Transfer of learning and, in certain cases, Data science.
Qing He combines subjects such as Probabilistic logic and Bayesian probability with his study of Machine learning.
His Recommender system research is multidisciplinary, incorporating elements of Generalization and Function.
His Data mining research also works with subjects such as
- Graph most often made with reference to Database transaction,
- Event together with Sequence.
Between 2017 and 2021, his most popular works were:
- A Comprehensive Survey on Transfer Learning (119 citations)
- Macrocycles as Ion Pair Receptors (61 citations)
- Macrocycles as Ion Pair Receptors (61 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Organic chemistry
Qing He mainly focuses on Artificial intelligence, Crystallography, Domain, Artificial neural network and Supramolecular chemistry.
Qing He has researched Artificial intelligence in several fields, including Machine learning, Pattern recognition and Natural language processing.
His Machine learning research integrates issues from Online advertising, Generator and Meta learning.
His Crystallography research includes themes of Ion, Redox, Pyrrole and Trifluoroacetic acid.
In his research, Data mining is intimately related to Database transaction, which falls under the overarching field of Domain.
His biological study spans a wide range of topics, including Combinatorial chemistry and Ion pairs.
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