D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 64 Citations 30,154 224 World Ranking 1199 National Ranking 703

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Artificial intelligence, Machine learning, Scientific discovery, Data mining and Pattern recognition. Pat Langley performs integrative Artificial intelligence and Simple research in his work. His study in the field of Feature selection, Decision tree and Active learning also crosses realms of Noise.

Pat Langley has researched Data mining in several fields, including Database and Data set. His Pattern recognition research includes elements of Bayesian programming, Bayesian average, Bayesian statistics and Bayesian inference. His biological study spans a wide range of topics, including Variable-order Bayesian network, Bayesian linear regression and Kernel.

His most cited work include:

  • Selection of relevant features and examples in machine learning (2577 citations)
  • Estimating continuous distributions in Bayesian classifiers (2435 citations)
  • An analysis of Bayesian classifiers (1040 citations)

What are the main themes of his work throughout his whole career to date?

Pat Langley mainly focuses on Artificial intelligence, Machine learning, Task, Cognitive science and Human–computer interaction. His Artificial intelligence study combines topics from a wide range of disciplines, such as Domain, Concept learning and Set. His Machine learning study combines topics in areas such as Process modeling and Data mining.

Pat Langley undertakes multidisciplinary studies into Task and Generality in his work. His study focuses on the intersection of Cognitive science and fields such as Cognitive architecture with connections in the field of Cognitive model. His work is dedicated to discovering how Human–computer interaction, User modeling are connected with Recommender system and other disciplines.

He most often published in these fields:

  • Artificial intelligence (58.75%)
  • Machine learning (29.04%)
  • Task (14.19%)

What were the highlights of his more recent work (between 2011-2020)?

  • Artificial intelligence (58.75%)
  • Cognitive science (8.91%)
  • Interpretation (2.97%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Artificial intelligence, Cognitive science, Interpretation, Task and Human–computer interaction. His Artificial intelligence study incorporates themes from Domain, Machine learning and Plan. In general Machine learning, his work in Variables and Regression analysis is often linked to Sensitivity and Product design linking many areas of study.

His research integrates issues of Cognitive systems, Key, Knowledge management and Social cognition in his study of Cognitive science. His study looks at the relationship between Task and fields such as Social psychology, as well as how they intersect with chemical problems. His Human–computer interaction research is multidisciplinary, incorporating perspectives in Closing, Web application, Data science and Systems biology.

Between 2011 and 2020, his most popular works were:

  • Explainable Agency for Intelligent Autonomous Systems. (74 citations)
  • The cognitive systems paradigm (40 citations)
  • Estimating Continuous Distributions in Bayesian Classifiers (27 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Cognitive science, Management science, Engineering design process, Epistemology and Agency. Management science is intertwined with Structure, Social planning and Work in his research. His Engineering design process investigation overlaps with other areas such as Predicate logic, Theoretical computer science, Design thinking, Representation and Answer set programming.

His studies deal with areas such as Closing, Cognitive systems and Field as well as Epistemology. Pat Langley focuses mostly in the field of Agency, narrowing it down to topics relating to Normative and, in certain cases, Relation. His research integrates issues of Intelligent agent, Cognitive architecture and Cognitive robotics in his study of Relation.

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.

Best Publications

Selection of relevant features and examples in machine learning

Avrim L. Blum;Pat Langley.
Artificial Intelligence (1997)

4277 Citations

Estimating continuous distributions in Bayesian classifiers

George H. John;Pat Langley.
uncertainty in artificial intelligence (1995)

4128 Citations

An analysis of Bayesian classifiers

Pat Langley;and Wayne Iba;Kevin Thompson.
national conference on artificial intelligence (1992)

1824 Citations

Scientific Discovery: Computational Explorations of the Creative Processes

Pat Langley;Herbert A. Simon;Gary L. Bradshaw;Jan M. Zytkow.
(1987)

1823 Citations

Editorial: On Machine Learning

Pat Langley.
Machine Learning (1986)

1534 Citations

Induction of selective Bayesian classifiers

Pat Langley;Stephanie Sage.
uncertainty in artificial intelligence (1994)

1047 Citations

Models of incremental concept formation

John H. Gennari;Pat Langley;Doug Fisher.
Artificial Intelligence (1993)

1038 Citations

Cognitive architectures: Research issues and challenges

Pat Langley;John E. Laird;Seth Rogers.
Cognitive Systems Research (2009)

904 Citations

Elements of Machine Learning

Pat Langley.
(1995)

862 Citations

Selection of Relevant Features in Machine Learning

Pat Langley.
national conference on artificial intelligence (1994)

841 Citations

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