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This book develops a framework that shows how uncertainty in
Artificial Intelligence (AI) expands and generalizes traditional
AI. It explores the uncertainties of knowledge and intelligence.
The authors focus on the importance of natural language - the
carrier of knowledge and intelligence, and introduce efficient
physical methods for data mining amd control. In this new edition,
we have more in-depth description of the models and methods, of
which the mathematical properties are proved strictly which make
these theories and methods more complete. The authors also
highlight their latest research results.
* This book is an updated version of a well-received book
previously published in Chinese by Science Press of China (the
first edition in 2006 and the second in 2013). It offers a
systematic and practical overview of spatial data mining, which
combines computer science and geo-spatial information science,
allowing each field to profit from the knowledge and techniques of
the other. To address the spatiotemporal specialties of spatial
data, the authors introduce the key concepts and algorithms of the
data field, cloud model, mining view, and Deren Li methods. The
data field method captures the interactions between spatial objects
by diffusing the data contribution from a universe of samples to a
universe of population, thereby bridging the gap between the data
model and the recognition model. The cloud model is a qualitative
method that utilizes quantitative numerical characters to bridge
the gap between pure data and linguistic concepts. The mining view
method discriminates the different requirements by using scale,
hierarchy, and granularity in order to uncover the anisotropy of
spatial data mining. The Deren Li method performs data
preprocessing to prepare it for further knowledge discovery by
selecting a weight for iteration in order to clean the observed
spatial data as much as possible. In addition to the essential
algorithms and techniques, the book provides application examples
of spatial data mining in geographic information science and remote
sensing. The practical projects include spatiotemporal video data
mining for protecting public security, serial image mining on
nighttime lights for assessing the severity of the Syrian Crisis,
and the applications in the government project 'the Belt and Road
Initiatives'.
This book develops a framework that shows how uncertainty in
Artificial Intelligence (AI) expands and generalizes traditional
AI. It explores the uncertainties of knowledge and intelligence.
The authors focus on the importance of natural language - the
carrier of knowledge and intelligence, and introduce efficient
physical methods for data mining amd control. In this new edition,
we have more in-depth description of the models and methods, of
which the mathematical properties are proved strictly which make
these theories and methods more complete. The authors also
highlight their latest research results.
* This book is an updated version of a well-received book
previously published in Chinese by Science Press of China (the
first edition in 2006 and the second in 2013). It offers a
systematic and practical overview of spatial data mining, which
combines computer science and geo-spatial information science,
allowing each field to profit from the knowledge and techniques of
the other. To address the spatiotemporal specialties of spatial
data, the authors introduce the key concepts and algorithms of the
data field, cloud model, mining view, and Deren Li methods. The
data field method captures the interactions between spatial objects
by diffusing the data contribution from a universe of samples to a
universe of population, thereby bridging the gap between the data
model and the recognition model. The cloud model is a qualitative
method that utilizes quantitative numerical characters to bridge
the gap between pure data and linguistic concepts. The mining view
method discriminates the different requirements by using scale,
hierarchy, and granularity in order to uncover the anisotropy of
spatial data mining. The Deren Li method performs data
preprocessing to prepare it for further knowledge discovery by
selecting a weight for iteration in order to clean the observed
spatial data as much as possible. In addition to the essential
algorithms and techniques, the book provides application examples
of spatial data mining in geographic information science and remote
sensing. The practical projects include spatiotemporal video data
mining for protecting public security, serial image mining on
nighttime lights for assessing the severity of the Syrian Crisis,
and the applications in the government project 'the Belt and Road
Initiatives'.
The information deluge currently assaulting us in the 21st century
is having a profound impact on our lifestyles and how we work. We
must constantly separate trustworthy and required information from
the massive amount of data we encounter each day. Through
mathematical theories, models, and experimental computations,
Artificial Intelligence with Uncertainty explores the uncertainties
of knowledge and intelligence that occur during the cognitive
processes of human beings. The authors focus on the importance of
natural language-the carrier of knowledge and intelligence-for
artificial intelligence (AI) study. This book develops a framework
that shows how uncertainty in AI expands and generalizes
traditional AI. It describes the cloud model, its uncertainties of
randomness and fuzziness, and the correlation between them. The
book also centers on other physical methods for data mining, such
as the data field and knowledge discovery state space. In addition,
it presents an inverted pendulum example to discuss reasoning and
control with uncertain knowledge as well as provides a cognitive
physics model to visualize human thinking with hierarchy. With
in-depth discussions on the fundamentals, methodologies, and
uncertainties in AI, this book explains and simulates human
thinking, leading to a better understanding of cognitive processes.
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