What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
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.
His most cited work include:
- Case-Based Reasoning: Experiences, Lessons and Future Directions (775 citations)
- Retrieval, reuse, revision and retention in case-based reasoning (458 citations)
- Categorizing Case-Base Maintenance: Dimensions and Directions (126 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (35.19%)
- Case-based reasoning (26.39%)
- Adaptation (19.91%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (35.19%)
- Machine learning (16.67%)
- Adaptation (19.91%)
In recent papers he was focusing on the following fields of study:
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.
Between 2013 and 2021, his most popular works were:
- Transfer Learning via Relational Type Matching (13 citations)
- Enhancing case-based regression with automatically-generated ensembles of adaptations (12 citations)
- A Study of Two-Phase Retrieval for Process-Oriented Case-Based Reasoning (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
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.
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