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Books > Computing & IT > Computer hardware & operating systems > Supercomputers
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Knowledge Guided Machine Learning - Accelerating Discovery using Scientific Knowledge and Data (Hardcover)
Loot Price: R2,986
Discovery Miles 29 860
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Knowledge Guided Machine Learning - Accelerating Discovery using Scientific Knowledge and Data (Hardcover)
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Expected to ship within 12 - 17 working days
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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. 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 by 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 by the broader community of
researchers and practitioners to realize the full potential of KGML
General
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