Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
Seiten
2022
Business Science Reference (Verlag)
978-1-7998-8351-7 (ISBN)
Business Science Reference (Verlag)
978-1-7998-8351-7 (ISBN)
Presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. The presents research of energy efficient accelerators for machine learning algorithms.
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.
| Erscheinungsdatum | 10.03.2022 |
|---|---|
| Sprache | englisch |
| Maße | 216 x 279 mm |
| Gewicht | 633 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| ISBN-10 | 1-7998-8351-5 / 1799883515 |
| ISBN-13 | 978-1-7998-8351-7 / 9781799883517 |
| Zustand | Neuware |
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