- We will send in 10–14 business days.
/images/books/12310975/9781799883500.jpg)
- Author: Veljko Milutinović
- Publisher: Engineering Science Reference
- Year: 2022
- ISBN-10: 1799883507
- ISBN-13: 9781799883500
- Format: 17.8 x 25.4 x 1.9 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms (e-book) (used book) | bookbook.eu
Reviews
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 22d.05:37:54
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Veljko Milutinović
- Publisher: Engineering Science Reference
- Year: 2022
- ISBN-10: 1799883507
- ISBN-13: 9781799883500
- Format: 17.8 x 25.4 x 1.9 cm, kieti viršeliai
- Language: English English
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.
Reviews