2014 - AAAI Distinguished Service Award In recognition of his outstanding work as Editor-in-Chief of AI Magazine, the journal of record for the AI community, for more than 15 years, his sustained service as AAAI Publications Chair, and his seminal work and service in the case-based reasoning and learning community.
His main research concerns Artificial intelligence, Case-based reasoning, Adaptation, Risk analysis and Information retrieval. His Artificial intelligence study frequently links to other fields, such as Creativity. His Case-based reasoning research includes themes of Qualitative reasoning, Management science, Adaptive reasoning, Reasoning system and Data science.
His work in Management science tackles topics such as Field which are related to areas like Search engine indexing. His research in Adaptation intersects with topics in Machine learning, Task and Reuse. David B. Leake combines subjects such as Design knowledge and Human–computer interaction with his study of Machine learning.
The scientist’s investigation covers issues in Artificial intelligence, Case-based reasoning, Adaptation, Machine learning and Knowledge management. His work carried out in the field of Artificial intelligence brings together such families of science as Natural language processing, Introspection and Set. His studies in Case-based reasoning integrate themes in fields like Model-based reasoning, Management science, Reasoning system and Similarity.
His Reasoning system course of study focuses on Qualitative reasoning and Adaptive reasoning and Verbal reasoning. His biological study spans a wide range of topics, including Reuse, Task and Data mining. His work deals with themes such as Domain and Data science, which intersect with Knowledge management.
David B. Leake mostly deals with Artificial intelligence, Machine learning, Adaptation, Case-based reasoning and Case base. When carried out as part of a general Artificial intelligence research project, his work on Similarity is frequently linked to work in Case finding, therefore connecting diverse disciplines of study. He has included themes like Graph database and Scalability in his Machine learning study.
He has researched Adaptation in several fields, including Ensemble learning, Categorical variable and Heuristic. The concepts of his Case-based reasoning study are interwoven with issues in Knowledge management, Artificial neural network, Task, Information retrieval and Data science. His Task research is multidisciplinary, incorporating elements of Word, Search engine indexing and Index.
David B. Leake mainly focuses on Artificial intelligence, Case base, Machine learning, Adaptation and Case-based reasoning. His study on Segmentation is often connected to Event as part of broader study in Artificial intelligence. His Case base research includes elements of Algorithm, Reduction and Feature.
The various areas that he examines in his Adaptation study include Function, Simple, Heuristic and Heuristic. His Case-based reasoning research is multidisciplinary, incorporating perspectives in Knowledge management, Structure, Task and Model-based reasoning. His Task research incorporates elements of Rank, Information retrieval and Component.
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.
Case-Based Reasoning: Experiences, Lessons and Future Directions
David B. Leake.
(1996)
Retrieval, reuse, revision and retention in case-based reasoning
Ramon Lopez De Mantaras;David McSherry;Derek Bridge;David Leake.
Knowledge Engineering Review (2005)
Creativity and learning in a case-based explainer
Roger C. Schank;David B. Leake.
Artificial Intelligence (1989)
Categorizing Case-Base Maintenance: Dimensions and Directions
David B. Leake;David C. Wilson.
Lecture Notes in Computer Science (1998)
Evaluating Explanations: A Content Theory
David B. Leake.
(1992)
Learning to Improve Case Adaption by Introspective Reasoning and CBR
David B. Leake;Andrew Kinley;David C. Wilson.
international conference on case based reasoning (1995)
Maintaining Case‐Based Reasoners: Dimensions and Directions
David C. Wilson;David B. Leake.
computational intelligence (2001)
Goal-driven learning
Ashwin Ram;David B. Leake.
(1995)
Managing, Mapping, and Manipulating Conceptual Knowledge*
Alberto Cafias;David B. Leake;David C. Wilson.
(1999)
Remembering Why to Remember: Performance-Guided Case-Base Maintenance
David B. Leake;David C. Wilson.
Lecture Notes in Computer Science (2000)
Profile was last updated on December 6th, 2021.
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