What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Ah-Hwee Tan mainly focuses on Artificial intelligence, Machine learning, Information retrieval, Adaptive resonance theory and Artificial neural network.
The Artificial intelligence study combines topics in areas such as Natural language processing and Pattern recognition.
The Overfitting research Ah-Hwee Tan does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Memetic algorithm, therefore creating a link between diverse domains of science.
Ah-Hwee Tan has researched Information retrieval in several fields, including Text mining and Image segmentation.
His research integrates issues of Recall, Semantic memory, Cognition, Cluster analysis and Robustness in his study of Adaptive resonance theory.
The concepts of his Artificial neural network study are interwoven with issues in Pattern recognition, Cognitive model, Software agent and System identification.
His most cited work include:
- Text Mining: The state of the art and the challenges (360 citations)
- A fast pruned-extreme learning machine for classification problem (276 citations)
- Rule Extraction: From Neural Architecture to Symbolic Representation (142 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Machine learning, Artificial neural network, Adaptive resonance theory and Reinforcement learning.
Ah-Hwee Tan combines subjects such as Cognition, Data mining and Pattern recognition with his study of Artificial intelligence.
In the subject of general Machine learning, his work in Decision tree is often linked to Set, thereby combining diverse domains of study.
His biological study spans a wide range of topics, including Deep learning and Problem domain.
His Adaptive resonance theory research is multidisciplinary, relying on both Semantic memory and Episodic memory.
His research investigates the connection with Cluster analysis and areas like Information retrieval which intersect with concerns in Natural language processing.
He most often published in these fields:
- Artificial intelligence (59.53%)
- Machine learning (26.07%)
- Artificial neural network (24.90%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (59.53%)
- Cluster analysis (15.18%)
- Adaptive resonance theory (17.12%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Cluster analysis, Adaptive resonance theory, Machine learning and Pattern recognition.
Ah-Hwee Tan merges Artificial intelligence with Activities of daily living in his research.
The study incorporates disciplines such as Probabilistic logic, Information retrieval, Residual and Robustness in addition to Cluster analysis.
His study in Adaptive resonance theory is interdisciplinary in nature, drawing from both Social media, Semantic memory and Episodic memory.
His Machine learning study integrates concerns from other disciplines, such as Frame and Data mining.
His Pattern recognition study incorporates themes from Electroencephalography, Facial recognition system, Image, Benchmark and Image restoration.
Between 2015 and 2021, his most popular works were:
- Benchmarking Single-Image Reflection Removal Algorithms (55 citations)
- Summarization of Egocentric Videos: A Comprehensive Survey (52 citations)
- CRRN: Multi-scale Guided Concurrent Reflection Removal Network (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His main research concerns Artificial intelligence, Pattern recognition, Cluster analysis, Automatic summarization and Image segmentation.
Ah-Hwee Tan has included themes like Machine learning and Cognition in his Artificial intelligence study.
Ah-Hwee Tan interconnects Decision support system and Inference in the investigation of issues within Machine learning.
Ah-Hwee Tan focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Image and, in some cases, Sparse approximation.
His Cluster analysis research incorporates elements of Anomaly detection, Probabilistic logic and Residual.
He combines subjects such as Segmentation and Multimedia with his study of Automatic summarization.
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