2004 - IEEE Fellow For contributions to machine intelligence, computer vision, and intelligent robotics.
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.
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.
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.
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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A new method for gray-level picture thresholding using the entropy of the histogram
J. N. Kapur;Prasanna K. Sahoo;Andrew K. C. Wong.
Graphical Models /graphical Models and Image Processing /computer Vision, Graphics, and Image Processing (1985)
A survey of thresholding techniques
P. K. Sahoo;S. Soltani;A. K.C. Wong;Y. C. Chen.
Graphical Models /graphical Models and Image Processing /computer Vision, Graphics, and Image Processing (1988)
Cost-sensitive boosting for classification of imbalanced data
Yanmin Sun;Mohamed S. Kamel;Andrew K. C. Wong;Yang Wang.
Pattern Recognition (2007)
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
Yanmin Sun;Andrew K. C. Wong;Mohamed S. Kamel.
International Journal of Pattern Recognition and Artificial Intelligence (2009)
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
Andrew K. C. Wong;Manlai You.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1985)
A gray-level threshold selection method based on maximum entropy principle
A.K.C. Wong;P.K. Sahoo.
systems man and cybernetics (1989)
Class-dependent discretization for inductive learning from continuous and mixed-mode data
J.Y. Ching;A.K.C. Wong;K.C.C. Chan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1995)
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
Andrew K. C. Wong;David K. Y. Chiu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1987)
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
Wai-Ho Au;Keith C. C. Chan;Andrew K. C. Wong;Yang Wang.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2005)
A survey of multiple sequence comparison methods.
S. C. Chan;A. K. C. Wong;D. K. Y. Chiu.
Bulletin of Mathematical Biology (1992)
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