What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Andrew W. Moore spends much of his time researching Artificial intelligence, Machine learning, Reinforcement learning, Algorithm and Statistics.
His Artificial intelligence study frequently draws connections between related disciplines such as Dynamic programming.
His Lazy learning, Model selection and Cross-validation study, which is part of a larger body of work in Machine learning, is frequently linked to Set, bridging the gap between disciplines.
His Reinforcement learning study integrates concerns from other disciplines, such as Instance-based learning and Mathematical optimization.
Andrew W. Moore usually deals with Algorithm and limits it to topics linked to Robot and Computational geometry.
His Q-learning research is multidisciplinary, incorporating perspectives in Learning classifier system, Computational learning theory and Bellman equation.
His most cited work include:
- Reinforcement learning: a survey (5581 citations)
- X-means: Extending K-means with Efficient Estimation of the Number of Clusters (1771 citations)
- Locally Weighted Learning (1587 citations)
What are the main themes of his work throughout his whole career to date?
Andrew W. Moore focuses on Artificial intelligence, Machine learning, Data mining, Algorithm and Theoretical computer science.
Many of his research projects under Artificial intelligence are closely connected to Set with Set, tying the diverse disciplines of science together.
His work deals with themes such as Dynamic programming, Mathematical optimization and Markov decision process, which intersect with Reinforcement learning.
His study in the fields of Semi-supervised learning, Active learning, Instance-based learning and Unsupervised learning under the domain of Machine learning overlaps with other disciplines such as Sparse matrix.
The concepts of his Data mining study are interwoven with issues in Baseline, Bayesian network and Statistical model.
His Algorithm research includes elements of Redshift, Sky and Expectation–maximization algorithm.
He most often published in these fields:
- Artificial intelligence (44.29%)
- Machine learning (26.67%)
- Data mining (22.38%)
What were the highlights of his more recent work (between 2006-2020)?
- Artificial intelligence (44.29%)
- Theoretical computer science (10.95%)
- Machine learning (26.67%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Theoretical computer science, Machine learning, Algorithm and Random walk.
In his research, Andrew W. Moore performs multidisciplinary study on Artificial intelligence and Space.
His study on Theoretical computer science also encompasses disciplines like
- Association rule learning, Decision list, Search algorithm and Local regression most often made with reference to Contingency table,
- Indifference graph, Random graph and Semi-supervised learning most often made with reference to Clustering coefficient.
When carried out as part of a general Machine learning research project, his work on Link analysis and Statistical classification is frequently linked to work in Generative model, Link data and Network structure, therefore connecting diverse disciplines of study.
His research in Algorithm intersects with topics in Discretization, Bayesian network and Joint probability distribution.
His Random walk study also includes
- Computation most often made with reference to Graph,
- Keyword search together with Shortest path problem and Nearest neighbor search.
Between 2006 and 2020, his most popular works were:
- Fast incremental proximity search in large graphs (91 citations)
- A tractable approach to finding closest truncated-commute-time neighbors in large graphs (75 citations)
- Efficient intra- and inter-night linking of asteroid detections using kd-trees (56 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Theoretical computer science, Random walk, Graph, Artificial intelligence and Machine learning.
Andrew W. Moore has researched Theoretical computer science in several fields, including Node, Clustering coefficient and Social network.
His Clustering coefficient research incorporates elements of Semi-supervised learning, Indifference graph and Random graph.
The study incorporates disciplines such as PageRank, Proximity measure and Pruning algorithm in addition to Graph.
His biological study spans a wide range of topics, including Data stream mining and Multivariate statistics.
His study on Model selection is often connected to Fraction as part of broader study in Machine learning.
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