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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.
Preface.
Introduction.
Introduction to Data Mining.
Classification Algorithms and Control-Flow Implementation.
Classification Algorithms and Dataflow Implementation.
Scientific Applications of Machine Learning Algorithms.
Business and Industrial Applications of Machine Learning Algorithms.
Implementation Details of Neural Networks Using Dataflow.
Implementation Details of Decision Tree Algorithms Using Dataflow.
Implementation Details of Rule-Based Algorithms Using Dataflow.
Implementation Details of Density-Based Algorithms Using Dataflow.
Issues Related to Acceleration of Algorithms.
Conclusion.
Glossary.
Related Readings.
About the Authors.
Index