Efficient Learning Machines
Theories, Concepts, and Applications for Engineers and System Designers
(Sprache: Englisch)
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient...
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Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.
Mariette Awad and Rahul Khanna's synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions.
Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms.
Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of
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multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
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Inhaltsverzeichnis zu „Efficient Learning Machines “
Chapter 1. Machine LearningChapter 2. Machine Learning and Knowledge Discovery
Chapter 3. Support Vector Machines for Classification
Chapter 4. Support Vector Regression
Chapter 5. Hidden Markov Model
Chapter 6. Bio-Inspired Computing: Swarm Intelligence
Chapter 7. Deep Neural Networks
Chapter 8. Cortical Algorithms
Chapter 9. Deep Learning
Chapter 10. Multiobjective Optimization
Chapter 11. Machine Learning in Action: Examples
Autoren-Porträt von Mariette Awad, Rahul Khanna
Rahul Khanna is currently a platform architect at IntelCorporation involved in development of energy efficient algorithms. Over thepast 17 years he has worked on server system software technologies includingplatform automation, power/thermal optimization techniques, reliability,optimization, and predictive methodologies. He has authored several technicalpapers and book chapters in the areas related to energy optimization,platform wireless interconnects, sensor networks, interconnect reliability, predictivemodeling, motion estimation, and security and holds 27 patents. He is also theco-inventor of the Intel IBIST methodology for High-Speed interconnect testing.His research interests include machine learning based power/thermaloptimization algorithms, narrow-channel high-speed wireless interconnects andinformation retrieval in dense sensor networks. Rahul is member of IEEE and therecipient of 3 Intel Achievement Awards for his contributions in areas relatedto advancements of platform technologies. He is the author of A Vision forPlatform Autonomy: Robust Frameworks for Systems.
Bibliographische Angaben
- Autoren: Mariette Awad , Rahul Khanna
- 2015, XIX, 268 Seiten, Maße: 17,8 x 25,4 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1430259892
- ISBN-13: 9781430259893
- Erscheinungsdatum: 07.05.2015
Sprache:
Englisch
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