What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
His primary areas of investigation include Data mining, Anomaly detection, Outlier, Artificial intelligence and Pattern recognition.
Particularly relevant to Temporal database is his body of work in Data mining.
Chang-Tien Lu regularly ties together related areas like Detection performance in his Anomaly detection studies.
His studies in Outlier integrate themes in fields like Data set and Identification.
His research in the fields of Feature learning and Thresholding overlaps with other disciplines such as Multi-task learning and Vocabulary.
His Pattern recognition study combines topics from a wide range of disciplines, such as Object and Algorithm.
His most cited work include:
- Advances in Spatial and Temporal Databases (424 citations)
- Survey of fraud detection techniques (270 citations)
- A Unified Approach to Detecting Spatial Outliers (178 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Artificial intelligence, Social media, Data science and Machine learning are his primary areas of study.
His research in Data mining intersects with topics in Feature learning, Outlier and Data set.
The concepts of his Artificial intelligence study are interwoven with issues in Graph and Pattern recognition.
Chang-Tien Lu combines subjects such as Object and Graph based with his study of Pattern recognition.
His research in Data science tackles topics such as Visualization which are related to areas like Database.
His studies deal with areas such as Event forecasting and Thresholding as well as Machine learning.
He most often published in these fields:
- Data mining (34.54%)
- Artificial intelligence (27.32%)
- Social media (18.56%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (27.32%)
- Data mining (34.54%)
- Machine learning (13.92%)
In recent papers he was focusing on the following fields of study:
Chang-Tien Lu focuses on Artificial intelligence, Data mining, Machine learning, Deep learning and Context.
His work on Graph expands to the thematically related Artificial intelligence.
His Data mining research is multidisciplinary, incorporating elements of Timestamp, Dependency and Incident management.
He usually deals with Incident management and limits it to topics linked to Similarity and Identification, Feature and Feature learning.
His Machine learning study combines topics in areas such as Class and Interactive Learning.
Chang-Tien Lu has included themes like Graph neural networks, Graph and Categorization in his Deep learning study.
Between 2018 and 2021, his most popular works were:
- Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data (16 citations)
- Predicting the hydrogen release ability of LiBH4-based mixtures by ensemble machine learning (14 citations)
- TapNet: Multivariate Time Series Classification with Attentional Prototypical Network (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of study are Artificial intelligence, Machine learning, Feature, Deep learning and Identification.
His Artificial intelligence research incorporates elements of Social media mining, PageRank and Graph.
The various areas that he examines in his Machine learning study include Interactive Learning and Social media.
His Feature study incorporates themes from Document classification, Dropout and Metric.
The Deep learning study combines topics in areas such as Contrast, Training set, Time series, Class and Multivariate statistics.
His research on Identification frequently connects to adjacent areas such as Scale.
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