What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
David Maxwell Chickering mainly focuses on Artificial intelligence, Bayesian network, Machine learning, Data mining and Bayesian probability.
His studies in Artificial intelligence integrate themes in fields like Natural language processing and Pattern recognition.
His studies deal with areas such as Variable-order Bayesian network, Graphical model, Theoretical computer science and Field as well as Bayesian network.
His Machine learning research includes themes of Smoothing, Classifier and Search algorithm.
His Data mining research includes elements of Language model, Graphical user interface and Collaborative filtering.
His work on Posterior probability as part of his general Bayesian probability study is frequently connected to Perfect map, thereby bridging the divide between different branches of science.
His most cited work include:
- Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (3169 citations)
- Learning Bayesian Networks is NP-Complete (817 citations)
- Optimal structure identification with greedy search (806 citations)
What are the main themes of his work throughout his whole career to date?
David Maxwell Chickering mainly investigates Artificial intelligence, Bayesian network, Machine learning, Data mining and Information retrieval.
The Artificial intelligence study combines topics in areas such as Natural language processing and Pattern recognition.
His Bayesian network research is multidisciplinary, relying on both Graphical model, Theoretical computer science, Bayesian probability and Search algorithm.
His research in Machine learning intersects with topics in Variable-order Bayesian network, Node, Posterior probability and Heuristic.
His Data mining study combines topics from a wide range of disciplines, such as Language model and Collaborative filtering.
His work in the fields of Information retrieval, such as Relevance, Ranking and Web search query, intersects with other areas such as Search advertising.
He most often published in these fields:
- Artificial intelligence (34.43%)
- Bayesian network (27.32%)
- Machine learning (24.59%)
What were the highlights of his more recent work (between 2011-2017)?
- Bayesian network (27.32%)
- Artificial intelligence (34.43%)
- Machine learning (24.59%)
In recent papers he was focusing on the following fields of study:
Bayesian network, Artificial intelligence, Machine learning, Theoretical computer science and Graphical model are his primary areas of study.
His Bayesian network research is multidisciplinary, incorporating elements of Node, Graph, Bayesian probability and Greedy algorithm.
His research investigates the link between Node and topics such as Posterior probability that cross with problems in Component.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Isolation, Pipeline and Natural language processing.
His study explores the link between Machine learning and topics such as Data set that cross with problems in Temporal database, Predictive analytics and Univariate.
David Maxwell Chickering works mostly in the field of Graphical model, limiting it down to concerns involving Algorithm and, occasionally, Markov chain, Model selection, Software and Smoothing.
Between 2011 and 2017, his most popular works were:
- Machine Teaching: A New Paradigm for Building Machine Learning Systems. (62 citations)
- Providing alternative content in a windowed environment (18 citations)
- Active machine learning (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Multi-task learning, Artificial intelligence, Instance-based learning, Active learning and Human–computer interaction.
His Multi-task learning study overlaps with Machine learning, Inductive transfer, Computational learning theory and Algorithmic learning theory.
His work on Error-driven learning as part of general Machine learning research is frequently linked to Retraining and Hyper-heuristic, thereby connecting diverse disciplines of science.
His multidisciplinary approach integrates Inductive transfer and Multimedia in his work.
His work on Feature as part of general Artificial intelligence study is frequently linked to Content, bridging the gap between disciplines.
His Human–computer interaction study incorporates themes from Interactive visualization, Graphical user interface, User interface, Representation and Interactive Learning.
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