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As information systems used for research and educational purposes
have become more complex, there has been an increase in the need
for new computing architecture. High performance and cloud
computing provide reliable and cost-effective information
technology infrastructure that enhances research and educational
processes. Handbook of Research on High Performance and Cloud
Computing in Scientific Research and Education presents the
applications of cloud computing in various settings, such as
scientific research, education, e-learning, ubiquitous learning,
and social computing. Providing various examples, practical
solutions, and applications of high performance and cloud
computing; this book is a useful reference for professionals and
researchers discovering the applications of information and
communication technologies in science and education, as well as
scholars seeking insight on how modern technologies support
scientific research.
Based on current literature and cutting-edge advances in the
machine learning field, there are four algorithms whose usage in
new application domains must be explored: neural networks, rule
induction algorithms, tree-based algorithms, and density-based
algorithms. A number of machine learning related algorithms have
been derived from these four algorithms. Consequently, they
represent excellent underlying methods for extracting hidden
knowledge from unstructured data, as essential data mining tasks.
Implementation of Machine Learning Algorithms Using Control-Flow
and Dataflow Paradigms presents widely used data-mining algorithms
and explains their advantages and disadvantages, their mathematical
treatment, applications, energy efficient implementations, and
more. It presents research of energy efficient accelerators for
machine learning algorithms. Covering topics such as control-flow
implementation, approximate computing, and decision tree
algorithms, this book is an essential resource for computer
scientists, engineers, students and educators of higher education,
researchers, and academicians.
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