Knowledge Guided Machine Learning
accelerating discovery using scientific knowledge and data
$96.91
- Paperback
430 pages
- Release Date
26 August 2024
Summary
Knowledge Guided Machine Learning: Bridging the Gap Between Data and Science
Given the widespread success of machine learning (ML) models in commercial applications, they are increasingly being considered as alternatives to science-based models across various disciplines. However, these “black-box” ML models often struggle due to limitations in training data and difficulties in generalizing to new scenarios. Consequently, there’s a growing interest in developing methods that integra…
Book Details
| ISBN-13: | 9780367698201 |
|---|---|
| ISBN-10: | 036769820X |
| Series: | Chapman & Hall/CRC Data Mining and Knowledge Discovery Series |
| Author: | Anuj Karpatne, Ramakrishnan Kannan, Vipin Kumar |
| Publisher: | Taylor & Francis Ltd |
| Imprint: | Chapman & Hall/CRC |
| Format: | Paperback |
| Number of Pages: | 430 |
| Release Date: | 26 August 2024 |
| Weight: | 920g |
| Dimensions: | 254mm x 178mm |
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About The Author
Anuj Karpatne
Anuj Karpatne is an Assistant Professor in the Department of Computer Science at Virginia Tech. His research focuses on pushing on the frontiers of knowledge-guided machine learning by combining scientific knowledge and data in the design and learning of machine learning methods to solve scientific and societally relevant problems.
Ramakrishnan Kannan is the group leader for Discrete Algorithms at Oak Ridge National Laboratory. His research expertise is in distributed machine learning and graph algorithms on HPC platforms and their application to scientific data with a specific interest for accelerating scientific discovery.
Vipin Kumar is a Regents Professor at the University of Minnesota’s Computer Science and Engineering Department. His current major research focus is on knowledge-guided machine learning and its applications to understanding the impact of human induced changes on the Earth and its environment.
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