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.
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.
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.
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.
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Selection of relevant features and examples in machine learning
Avrim L. Blum;Pat Langley.
Artificial Intelligence (1997)
Estimating continuous distributions in Bayesian classifiers
George H. John;Pat Langley.
uncertainty in artificial intelligence (1995)
An analysis of Bayesian classifiers
Pat Langley;and Wayne Iba;Kevin Thompson.
national conference on artificial intelligence (1992)
Scientific Discovery: Computational Explorations of the Creative Processes
Pat Langley;Herbert A. Simon;Gary L. Bradshaw;Jan M. Zytkow.
Editorial: On Machine Learning
Machine Learning (1986)
Induction of selective Bayesian classifiers
Pat Langley;Stephanie Sage.
uncertainty in artificial intelligence (1994)
Models of incremental concept formation
John H. Gennari;Pat Langley;Doug Fisher.
Artificial Intelligence (1993)
Cognitive architectures: Research issues and challenges
Pat Langley;John E. Laird;Seth Rogers.
Cognitive Systems Research (2009)
Elements of Machine Learning
Selection of Relevant Features in Machine Learning
national conference on artificial intelligence (1994)
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