Efficient learning machines [electronic resource] : theories, concepts, and applications for engineers and system Designers / Mariette Awad, Rahul Khanna.
2015
Q325.5 .A92 2015eb
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Title
Efficient learning machines [electronic resource] : theories, concepts, and applications for engineers and system Designers / Mariette Awad, Rahul Khanna.
Author
Awad, Mariette, author.
ISBN
9781430259909 electronic book
1430259906 electronic book
9781430259893
1430259906 electronic book
9781430259893
Published
[New York] : Apress Open, [2015]
Language
English
Description
1 online resource : illustrations.
Item Number
10.1007/978-1-4302-5990-9 doi
Call Number
Q325.5 .A92 2015eb
Dewey Decimal Classification
006.3/1
Summary
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{u2019}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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Added Author
Khanna, Rahul, 1966- author.
Series
Expert's voice in machine learning.
Available in Other Form
Print version: 9781430259893
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