Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
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| Title: | Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms |
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| Description: | 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. |
| Authors: | Veljko Milutinović, Nenad Mitić, Aleksandar Kartelj, Miloš Kotlar |
| Resource Type: | eBook. |
| Subjects: | Algorithms, Data mining, Data flow computing, Machine learning |
| Categories: | COMPUTERS / Programming / Algorithms, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Machine Learning |
| Database: | eBook Academic Collection (EBSCOhost) |
| Abstract: | 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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| ISBN: | 9781799883500 9781799883524 9781799883531 |
