What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
His main research concerns Data mining, Database transaction, Efficient algorithm, Scalability and Pruning.
He integrates Data mining and Execution time in his research.
His studies deal with areas such as Space, Computation and Field as well as Database transaction.
His Scalability research is multidisciplinary, incorporating perspectives in Knowledge extraction, Skyline and Pattern recognition.
As a member of one scientific family, Jerry Chun-Wei Lin mostly works in the field of Pruning, focusing on Measure and, on occasion, Timestamp.
The study incorporates disciplines such as Machine learning and Fast algorithm in addition to Artificial intelligence.
His most cited work include:
- The SPMF Open-Source Data Mining Library Version 2 (218 citations)
- A survey of itemset mining (100 citations)
- EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining (80 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Pruning, Database transaction, Artificial intelligence and Structure are his primary areas of study.
He undertakes interdisciplinary study in the fields of Data mining and Speedup through his works.
Jerry Chun-Wei Lin combines subjects such as Field, Uncertain data and Sequential Pattern Mining with his study of Pruning.
When carried out as part of a general Database transaction research project, his work on Utility mining is frequently linked to work in Profit, therefore connecting diverse disciplines of study.
His work deals with themes such as Natural language processing, Machine learning and Pattern recognition, which intersect with Artificial intelligence.
Jerry Chun-Wei Lin incorporates Projection and Extension in his research.
He most often published in these fields:
- Data mining (62.61%)
- Pruning (23.71%)
- Database transaction (21.28%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (19.15%)
- Data mining (62.61%)
- Pruning (23.71%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Data mining, Pruning, Database transaction and Artificial neural network.
His research integrates issues of Natural language processing, Machine learning and Pattern recognition in his study of Artificial intelligence.
His work on Knowledge extraction as part of general Data mining research is frequently linked to Graph based, thereby connecting diverse disciplines of science.
Jerry Chun-Wei Lin interconnects Affinity analysis, Space, Field and Fuzzy logic in the investigation of issues within Pruning.
His Database transaction research integrates issues from Computer cluster, Information sensitivity and Information privacy.
Jerry Chun-Wei Lin has included themes like Tree and Tree structure in his Scalability study.
Between 2020 and 2021, his most popular works were:
- A Survey of Utility-Oriented Pattern Mining (47 citations)
- ASRNN: A recurrent neural network with an attention model for sequence labeling (18 citations)
- ASRNN: A recurrent neural network with an attention model for sequence labeling (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His main research concerns Artificial intelligence, Deep learning, Data mining, Sharpe ratio and Artificial neural network.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Sequence labeling.
His Deep learning study also includes
- Cluster analysis that connect with fields like Object detection, Feature extraction, Convolutional neural network and Particle swarm optimization,
- Anomaly detection and related Convolution and Knowledge extraction.
His biological study spans a wide range of topics, including Field and Pruning.
His Artificial neural network research includes themes of Named-entity recognition, Parsing, Chunking and Conditional random field.
His Machine learning research is multidisciplinary, incorporating elements of Human behavior and Differential evolution.
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