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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.
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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