Knowledge-Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) 1st Edition
Author: Anuj Karpatne,Ramakrishnan Kannan,Vipin Kumar (Editor)
Publisher finelybook 出版社: Chapman and Hall/CRC; 1st edition (August 15, 2022)
Language 语言: English
Print Length 页数: 430 pages
ISBN-10: 0367693410
ISBN-13: 9780367693411
Book Description
By finelybook
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 Author: 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 Author: leading researchers.
KEY FEATURES
First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields
Accessible to a broad audience in data science and scientific and engineering fields
Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains
Contains chapters Author: leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives
Enables cross-pollination of KGML problem formulations and research methods across disciplines
Highlights critical gaps that require further investigation Author: the broader community of researchers and practitioners to realize the full potential of KGML
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