What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Data mining, Pattern recognition, Algorithm and Machine learning are his primary areas of study.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Discretization, Hypergraph and Computer vision.
In his work, Similarity measure, Probabilistic logic and Missing data is strongly intertwined with Inference, which is a subfield of Data mining.
Andrew K. C. Wong has researched Pattern recognition in several fields, including Principle of maximum entropy and Data set.
His research investigates the link between Principle of maximum entropy and topics such as Entropy that cross with problems in Gray level, Histogram matching, Image histogram, Histogram and Graphics.
His Machine learning study combines topics in areas such as Classifier and Inductive transfer.
His most cited work include:
- A new method for gray-level picture thresholding using the entropy of the histogram (2734 citations)
- A survey of thresholding techniques (2288 citations)
- Cost-sensitive boosting for classification of imbalanced data (919 citations)
What are the main themes of his work throughout his whole career to date?
Andrew K. C. Wong focuses on Artificial intelligence, Pattern recognition, Data mining, Machine learning and Computer vision.
His study in Probabilistic logic, Cognitive neuroscience of visual object recognition, Object, Pattern recognition and Feature are all subfields of Artificial intelligence.
His studies in Pattern recognition integrate themes in fields like Entropy, Texture and Supervised learning.
His study looks at the relationship between Data mining and topics such as Cluster analysis, which overlap with Protein family and Algorithm.
The Machine learning study combines topics in areas such as Classifier, Knowledge extraction and Knowledge acquisition.
While the research belongs to areas of Computer vision, he spends his time largely on the problem of Hypergraph, intersecting his research to questions surrounding Knowledge representation and reasoning, Theoretical computer science, Graph theory and Graph.
He most often published in these fields:
- Artificial intelligence (51.95%)
- Pattern recognition (24.68%)
- Data mining (21.65%)
What were the highlights of his more recent work (between 2009-2021)?
- Artificial intelligence (51.95%)
- Computational biology (8.23%)
- Protein family (6.49%)
In recent papers he was focusing on the following fields of study:
Andrew K. C. Wong mainly focuses on Artificial intelligence, Computational biology, Protein family, Pattern recognition and Data mining.
He has included themes like Machine learning and Protein–protein interaction in his Artificial intelligence study.
The Machine learning study combines topics in areas such as Class and Interpretation.
His Protein family study incorporates themes from Amino acid, Protein function prediction, Hierarchical clustering, Entropy and Protein sequencing.
Andrew K. C. Wong combines subjects such as Supervised learning, Algorithm design and Image retrieval with his study of Pattern recognition.
His work on Association rule learning as part of his general Data mining study is frequently connected to Suffix tree, thereby bridging the divide between different branches of science.
Between 2009 and 2021, his most popular works were:
- Apolipoprotein-E (Apoe) ε4 and cognitive decline over the adult life course. (54 citations)
- Discovery of Temporal Associations in Multivariate Time Series (31 citations)
- Integrated torque vectoring and power management framework for electric vehicles (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Pattern recognition, Data mining, Protein family and Image retrieval.
His research links Multivariate statistics with Artificial intelligence.
The study incorporates disciplines such as Algorithm design, Computer vision and Sequence in addition to Pattern recognition.
His Segmentation, Feature and Image processing study in the realm of Computer vision connects with subjects such as Initialization and Convergence.
He has researched Data mining in several fields, including Machine learning, Cluster analysis and Genomics.
His Sequence alignment research includes themes of Entropy and Probabilistic logic.
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