Efficient learning machines : theories, concepts, and applications for engineers and system designers

著者

    • Awad, Mariette
    • Khanna, Rahul

書誌事項

Efficient learning machines : theories, concepts, and applications for engineers and system designers

Mariette Awad, Rahul Khanna

(The expert's voice in machine learning)

Apress Open, c2015

  • : pbk

大学図書館所蔵 件 / 1

この図書・雑誌をさがす

注記

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

Chapter 1. Machine LearningChapter 2. Machine Learning and Knowledge DiscoveryChapter 3. Support Vector Machines for ClassificationChapter 4. Support Vector RegressionChapter 5. Hidden Markov ModelChapter 6. Bio-Inspired Computing: Swarm IntelligenceChapter 7. Deep Neural NetworksChapter 8. Cortical AlgorithmsChapter 9. Deep LearningChapter 10. Multiobjective OptimizationChapter 11. Machine Learning in Action: Examples

「Nielsen BookData」 より

関連文献: 1件中  1-1を表示

詳細情報

  • NII書誌ID(NCID)
    BB21593808
  • ISBN
    • 9781430259893
  • LCCN
    2015473127
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    [New York]
  • ページ数/冊数
    xix, 248 p.
  • 大きさ
    26 cm
  • 分類
  • 件名
  • 親書誌ID
ページトップへ