What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Anomaly detection and Data mining.
Kai Ming Ting undertakes interdisciplinary study in the fields of Artificial intelligence and Stacking through his works.
His work on Cost sensitive, Boosting, Averaged one-dependence estimators and Naive Bayes classifier as part of general Machine learning research is often related to Generalization, thus linking different fields of science.
His work on Classifier as part of general Pattern recognition research is frequently linked to Spacetime, thereby connecting diverse disciplines of science.
His Anomaly detection research is multidisciplinary, incorporating elements of Time complexity, Random forest and Binary tree.
His Data mining study combines topics in areas such as CURE data clustering algorithm, Canopy clustering algorithm, FLAME clustering, Fuzzy clustering and Cluster analysis.
His most cited work include:
- Isolation Forest (1283 citations)
- Isolation-Based Anomaly Detection (523 citations)
- Issues in stacked generalization (501 citations)
What are the main themes of his work throughout his whole career to date?
Kai Ming Ting mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Data mining and Cluster analysis.
Kai Ming Ting combines topics linked to Time complexity with his work on Artificial intelligence.
His Machine learning study frequently draws connections between adjacent fields such as Training set.
His Pattern recognition research is multidisciplinary, relying on both Subspace topology and Kernel.
In his work, Model selection and Feature is strongly intertwined with Estimator, which is a subfield of Data mining.
His Cluster analysis research incorporates elements of Algorithm and Heuristics.
He most often published in these fields:
- Artificial intelligence (64.86%)
- Machine learning (39.19%)
- Pattern recognition (31.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (64.86%)
- Measure (16.22%)
- Pattern recognition (31.08%)
In recent papers he was focusing on the following fields of study:
Kai Ming Ting focuses on Artificial intelligence, Measure, Pattern recognition, Kernel and Algorithm.
Kai Ming Ting combines subjects such as Machine learning and Parameterized complexity with his study of Artificial intelligence.
His Machine learning study incorporates themes from Learning methods and Class information.
Kai Ming Ting integrates many fields, such as Measure and engineering, in his works.
His Pattern recognition study integrates concerns from other disciplines, such as Pixel and Anomaly.
The concepts of his Kernel study are interwoven with issues in Cluster analysis, Data point and Kernel.
Between 2018 and 2021, his most popular works were:
- Nearest-neighbour-induced isolation similarity and its impact on density-based clustering (6 citations)
- Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms (6 citations)
- Isolation Set-Kernel and Its Application to Multi-Instance Learning (5 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Algorithm, Measure, Feature, Cluster analysis and Similarity.
His Feature research includes elements of Subspace topology and Kernel density estimation, Estimator.
He has included themes like Probability mass function and Euclidean distance in his Cluster analysis study.
The various areas that Kai Ming Ting examines in his Similarity study include Representation, Word and Text mining, Data mining.
Kai Ming Ting has researched Kernel in several fields, including Similarity, Computation, Kernel and Pattern recognition.
His Isolation study spans across into fields like Speedup, Support vector machine, Tree, Artificial intelligence and DBSCAN.
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