What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Data stream mining, Data mining, Artificial intelligence, Machine learning and Concept drift.
His Data stream mining research incorporates elements of Incremental learning, Wireless sensor network, Cluster analysis, Unsupervised learning and Data science.
The various areas that João Gama examines in his Data mining study include Data stream clustering, Change detection, Ensemble learning and Data set.
His studies in Artificial intelligence integrate themes in fields like Simple and Pattern recognition.
His Online machine learning, Statistical classification and Supervised learning study in the realm of Machine learning connects with subjects such as Online and offline.
In Concept drift, João Gama works on issues like Adaptation, which are connected to Categorization, Quality and Data modeling.
His most cited work include:
- A survey on concept drift adaptation (1287 citations)
- Learning with Drift Detection (833 citations)
- Knowledge Discovery from Data Streams. (546 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Data mining, Data stream mining, Machine learning and Data science.
The concepts of his Artificial intelligence study are interwoven with issues in Task, Regression and Pattern recognition.
João Gama has researched Data mining in several fields, including Data stream clustering, Regression analysis, Decision rule and Cluster analysis.
His research in Data stream mining is mostly concerned with Concept drift.
His Machine learning study often links to related topics such as Set.
His studies deal with areas such as Wireless sensor network and Knowledge extraction as well as Data science.
He most often published in these fields:
- Artificial intelligence (37.69%)
- Data mining (34.20%)
- Data stream mining (33.99%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (37.69%)
- Data stream mining (33.99%)
- Data science (14.38%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Data stream mining, Data science, Data mining and Machine learning.
His study connects Pattern recognition and Artificial intelligence.
A large part of his Data stream mining studies is devoted to Concept drift.
He interconnects Quality, Point of interest, Deep learning, Evolving networks and Big data in the investigation of issues within Data science.
His Data mining research includes themes of STREAMS and Cluster analysis.
His Machine learning study combines topics in areas such as Conditional probability, Credit history and Set.
Between 2017 and 2021, his most popular works were:
- Learning under Concept Drift: A Review (128 citations)
- Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview (31 citations)
- Social network analysis: An overview (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Data science, Artificial intelligence, Data stream mining, Data mining and Recommender system.
The Data science study combines topics in areas such as Quality, Social network analysis, Evolving networks, Automatic summarization and Big data.
His research integrates issues of Natural language processing, Machine learning and Pattern recognition in his study of Artificial intelligence.
His work in the fields of Data stream mining, such as Concept drift, overlaps with other areas such as Quantitative Concept.
His study in Data mining is interdisciplinary in nature, drawing from both Cardinality, Task, Denial-of-service attack and Cluster analysis.
His Recommender system study incorporates themes from Node, Stochastic process and Preference.
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