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000726825 019__ $$a910936545
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000726825 1001_ $$aAwad, Mariette,$$eauthor.
000726825 24510 $$aEfficient learning machines$$h[electronic resource] :$$btheories, concepts, and applications for engineers and system Designers /$$cMariette Awad, Rahul Khanna.
000726825 264_1 $$a[New York] :$$bApress Open,$$c[2015]
000726825 300__ $$a1 online resource :$$billustrations.
000726825 336__ $$atext$$btxt$$2rdacontent
000726825 337__ $$acomputer$$bc$$2rdamedia
000726825 338__ $$aonline resource$$bcr$$2rdacarrier
000726825 4901_ $$aThe expert's voice in machine learning
000726825 504__ $$aIncludes bibliographical references and index.
000726825 506__ $$aAccess limited to authorized users.
000726825 520__ $$aMachine 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.
000726825 650_0 $$aMachine learning.
000726825 7001_ $$aKhanna, Rahul,$$d1966-$$eauthor.
000726825 77608 $$iPrint version:$$z9781430259893
000726825 830_0 $$aExpert's voice in machine learning.
000726825 852__ $$bebk
000726825 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-1-4302-5990-9$$zOnline Access$$91397441.1
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