What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Zhongzhi Shi mostly deals with Artificial intelligence, Machine learning, Data mining, Theoretical computer science and Extreme learning machine.
His study looks at the intersection of Artificial intelligence and topics like Pattern recognition with Non-negative matrix factorization.
His research integrates issues of Generalization, Computation and Training set in his study of Machine learning.
His biological study spans a wide range of topics, including Task and Parallel computing.
Zhongzhi Shi interconnects Peer-to-peer, Task, Topology, Consistency and Synchronization in the investigation of issues within Theoretical computer science.
His work carried out in the field of Extreme learning machine brings together such families of science as Feature, Population-based incremental learning and Kernel.
His most cited work include:
- THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY (268 citations)
- Information entropy, rough entropy and knowledge granulation in incomplete information systems (222 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?
His main research concerns Artificial intelligence, Pattern recognition, Data mining, Machine learning and Algorithm.
His work in Feature extraction, Image retrieval, Support vector machine, Probabilistic latent semantic analysis and Cluster analysis is related to Artificial intelligence.
His Probabilistic latent semantic analysis study necessitates a more in-depth grasp of Natural language processing.
His studies deal with areas such as Contextual image classification and Feature, Computer vision as well as Pattern recognition.
The study incorporates disciplines such as Process and Information retrieval in addition to Data mining.
He works in the field of Machine learning, focusing on Extreme learning machine in particular.
He most often published in these fields:
- Artificial intelligence (46.46%)
- Pattern recognition (20.13%)
- Data mining (17.92%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (46.46%)
- Machine learning (16.81%)
- Pattern recognition (20.13%)
In recent papers he was focusing on the following fields of study:
Zhongzhi Shi mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Data mining and Cluster analysis.
All of his Artificial intelligence and Extreme learning machine, Deep learning, Granular computing, Pattern recognition and Artificial neural network investigations are sub-components of the entire Artificial intelligence study.
His work deals with themes such as Classifier, Inference and Identification, which intersect with Machine learning.
His Pattern recognition research integrates issues from Transfer of learning, Field and Automatic image annotation.
As a part of the same scientific family, he mostly works in the field of Data mining, focusing on Probabilistic latent semantic analysis and, on occasion, Annotation and Image retrieval.
Zhongzhi Shi works mostly in the field of Cluster analysis, limiting it down to topics relating to Algorithm and, in certain cases, Data set and Rough set.
Between 2013 and 2021, his most popular works were:
- Pessimistic rough set based decisions: A multigranulation fusion strategy (104 citations)
- Clustering in extreme learning machine feature space (72 citations)
- Learning deep representations via extreme learning machines (66 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Extreme learning machine and Cluster analysis.
His Generalization research extends to the thematically linked field of Artificial intelligence.
His work on Feature as part of general Machine learning study is frequently linked to Joint probability distribution, bridging the gap between disciplines.
In the subject of general Pattern recognition, his work in Support vector machine and Semi-supervised learning is often linked to Original data, thereby combining diverse domains of study.
His research investigates the connection between Extreme learning machine and topics such as Population-based incremental learning that intersect with problems in Online machine learning, Unsupervised learning and Learning classifier system.
His Cluster analysis research is multidisciplinary, incorporating perspectives in Mixture model and Algorithm.
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