What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Hau-San Wong mostly deals with Artificial intelligence, Cluster analysis, Data mining, Pattern recognition and Machine learning.
The study of Artificial intelligence is intertwined with the study of Computer vision in a number of ways.
His work on Rand index is typically connected to Dimension as part of general Cluster analysis study, connecting several disciplines of science.
Hau-San Wong interconnects Correlation clustering, Algorithm design, Clustering high-dimensional data and Consensus clustering in the investigation of issues within Data mining.
His Clustering high-dimensional data study combines topics in areas such as Time complexity, Subspace topology and Ensemble learning.
His Pattern recognition research includes elements of Face and Feature.
His most cited work include:
- Graph-based consensus clustering for class discovery from gene expression data (143 citations)
- Letters: Face and palmprint feature level fusion for single sample biometrics recognition (113 citations)
- Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering (101 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Cluster analysis, Machine learning and Data mining.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
In his study, Search engine indexing is strongly linked to Histogram, which falls under the umbrella field of Pattern recognition.
His Cluster analysis research is multidisciplinary, incorporating perspectives in Algorithm and Unsupervised learning.
His work carried out in the field of Machine learning brings together such families of science as Training set and Robustness.
His Data mining research incorporates elements of Correlation, Algorithm design, Clustering high-dimensional data, Constrained clustering and Affinity propagation.
He most often published in these fields:
- Artificial intelligence (67.14%)
- Pattern recognition (35.24%)
- Cluster analysis (28.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (67.14%)
- Machine learning (24.76%)
- Pattern recognition (35.24%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Pattern recognition, Cluster analysis and Regularization.
His Artificial intelligence study frequently draws parallels with other fields, such as Data modeling.
Hau-San Wong combines subjects such as Survival analysis and Robustness with his study of Machine learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification, Image, Image translation and Sample space.
His studies deal with areas such as Data mining, Data mapping, Encoder, Similarity and Unsupervised learning as well as Cluster analysis.
His work in Regularization covers topics such as Labeled data which are related to areas like Generative grammar and Generative model.
Between 2018 and 2021, his most popular works were:
- Model Adaptation: Unsupervised Domain Adaptation Without Source Data (33 citations)
- Adaptive Semi-Supervised Classifier Ensemble for High Dimensional Data Classification (15 citations)
- Hybrid Classifier Ensemble for Imbalanced Data (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Hau-San Wong mostly deals with Artificial intelligence, Cluster analysis, Machine learning, Classifier and Regularization.
His Artificial intelligence research is multidisciplinary, incorporating elements of Data mapping and Pattern recognition.
He has included themes like Subspace topology, Redundancy, Data mining and Search engine in his Cluster analysis study.
His Machine learning study frequently involves adjacent topics like Data modeling.
The study incorporates disciplines such as Ensemble learning, Training set and Linear subspace in addition to Classifier.
His research integrates issues of Deep learning and Discriminative model in his study of Regularization.
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