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Efficient Learning Machines

Theories, Concepts, and Applications for Engineers and System Designers

Authors: Awad, Mariette, Khanna, Rahul

  • Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques.

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  • ISBN 978-1-4302-5990-9
  • This book is an open access book, you can download it for free on link.springer.com
Softcover $39.99
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  • ISBN 978-1-4302-5989-3
  • Free shipping for individuals worldwide
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About this book

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 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.

About the authors

Rahul Khanna is a platform architect at Intel Corporation involved in development of energy-efficient algorithms. Over the past 17 years he has worked on server system software technologies, including platform automation, power/thermal optimization techniques, reliability, optimization, and predictive methodologies. He has authored numerous technical papers and book chapters in the areas related to energy optimization, platform wireless interconnects, sensor networks, interconnect reliability, predictive modeling, motion estimation, and security. He holds 27 patents. He is the co-inventor of the Intel IBIST methodology for High-Speed interconnect testing. His research interests include machine learning-based power/thermal optimization algorithms, narrow-channel high-speed wireless interconnects, and information retrieval in dense sensor networks. Rahul is member of IEEE and the recipient of three Intel Achievement Awards for his contributions in areas related to advancements of platform technologies. He is the author of A Vision for Platform Autonomy: Robust Frameworks for Systems.

Table of contents (11 chapters)

  • Machine Learning

    Awad, Mariette (et al.)

    Pages 1-18

  • Machine Learning and Knowledge Discovery

    Awad, Mariette (et al.)

    Pages 19-38

  • Support Vector Machines for Classification

    Awad, Mariette (et al.)

    Pages 39-66

  • Support Vector Regression

    Awad, Mariette (et al.)

    Pages 67-80

  • Hidden Markov Model

    Awad, Mariette (et al.)

    Pages 81-104

Buy this book

eBook  
  • ISBN 978-1-4302-5990-9
  • This book is an open access book, you can download it for free on link.springer.com
Softcover $39.99
price for USA
  • ISBN 978-1-4302-5989-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Efficient Learning Machines
Book Subtitle
Theories, Concepts, and Applications for Engineers and System Designers
Authors
Copyright
2015
Publisher
Apress
Copyright Holder
The Editor(s) (if applicable) and The Author(s)
eBook ISBN
978-1-4302-5990-9
DOI
10.1007/978-1-4302-5990-9
Softcover ISBN
978-1-4302-5989-3
Edition Number
1
Number of Pages
XIX, 268
Number of Illustrations and Tables
88 b/w illustrations
Topics