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- Andrew W. Moore

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Computer Science
H-index
59
Citations
25,530
154
World Ranking
1605
National Ranking
90

- 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.

- 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)

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.

- Artificial intelligence (44.29%)
- Machine learning (26.67%)
- Data mining (22.38%)

- Artificial intelligence (44.29%)
- Theoretical computer science (10.95%)
- Machine learning (26.67%)

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.

- 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)

- 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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Reinforcement learning: a survey

Leslie Pack Kaelbling;Michael L. Littman;Andrew W. Moore.

Journal of Artificial Intelligence Research **(1996)**

8544 Citations

X-means: Extending K-means with Efficient Estimation of the Number of Clusters

Dan Pelleg;Andrew W. Moore.

international conference on machine learning **(2000)**

3219 Citations

Locally weighted learning for control

Christopher G. Atkeson;Andrew W. Moore;Stefan Schaal.

Artificial Intelligence Review **(1997)**

2450 Citations

Locally Weighted Learning

Christopher G. Atkeson;Andrew W. Moore;Stefan Schaal.

Artificial Intelligence Review **(1997)**

2221 Citations

Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time

Andrew W. Moore;Christopher G. Atkeson.

Machine Learning **(1993)**

1091 Citations

Generalization in Reinforcement Learning: Safely Approximating the Value Function

Justin A. Boyan;Andrew W. Moore.

neural information processing systems **(1994)**

844 Citations

An Investigation of Practical Approximate Nearest Neighbor Algorithms

Ting Liu;Andrew W. Moore;Ke Yang;Alexander G. Gray.

neural information processing systems **(2004)**

530 Citations

Accelerating exact k-means algorithms with geometric reasoning

Dan Pelleg;Andrew Moore.

knowledge discovery and data mining **(1999)**

497 Citations

Dynamic social network analysis using latent space models

Purnamrita Sarkar;Andrew W. Moore.

Sigkdd Explorations **(2005)**

468 Citations

The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces

Andrew W. Moore.

neural information processing systems **(1993)**

405 Citations

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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