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(Ebook) Knowledge-Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) by Anuj Karpatne, Ramakrishnan Kannan, Vipin Kumar ISBN 9780367693411, 0367693410

  • SKU: EBN-44169202
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Instant download (eBook) Knowledge-Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) after payment.
Authors:Anuj Karpatne, Ramakrishnan Kannan, Vipin Kumar
Pages:430 pages.
Year:2022
Editon:1
Publisher:Chapman and Hall/CRC
Language:english
File Size:91.33 MB
Format:pdf
ISBNS:9780367693411, 0367693410
Categories: Ebooks

Product desciption

(Ebook) Knowledge-Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) by Anuj Karpatne, Ramakrishnan Kannan, Vipin Kumar ISBN 9780367693411, 0367693410

Given their tremendous success in commercial applications, Machine Learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these “black-box” ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific Knowledge-guided ML (KGML), seeks a distinct departure from existing “data-only” or “scientific knowledge-only” methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field.
"Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data" provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML, using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers.
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