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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Text Mining: The state of the art and the challenges
Ah-Hwee Tan;Heng Mui;Keng Terrace.
A fast pruned-extreme learning machine for classification problem
Hai-Jun Rong;Yew-Soon Ong;Ah-Hwee Tan;Zexuan Zhu.
Rule Extraction: From Neural Architecture to Symbolic Representation
Gail A. Carpenter;Ah-Hwee Tan.
Connection Science (1995)
Learning and inferencing in user ontology for personalized Semantic Web search
Xing Jiang;Ah-Hwee Tan.
Information Sciences (2009)
On Quantitative Evaluation of Clustering Systems.
Ji He;Ah-Hwee Tan;Chew Lim Tan;Sam Yuan Sung.
Clustering and Information Retrieval (2004)
CRCTOL: A semantic-based domain ontology learning system
Xing Jiang;Ah-Hwee Tan.
Journal of the Association for Information Science and Technology (2010)
Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback
Ah-Hwee Tan;Ning Lu;Dan Xiao.
IEEE Transactions on Neural Networks (2008)
Modelling situation awareness for Context-aware Decision Support
Yu-Hong Feng;Teck-Hou Teng;Ah-Hwee Tan.
Expert Systems With Applications (2009)
Integrated cognitive architectures: a survey
Hui-Qing Chong;Ah-Hwee Tan;Gee-Wah Ng.
Artificial Intelligence Review (2007)
Method and system for discovering knowledge from text documents
Ah Hwee Tan;Rajaraman Kanagasabai.
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: