Efficient Learning Machines

Theories, Concepts, and Applications for Engineers and System Designers

by Mariette Awad, Rahul Khanna

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Book Description

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.

This open book is licensed under a Creative Commons License (CC BY-NC-ND). You can download Efficient Learning Machines ebook for free in PDF format (8.2 MB).

Table of Contents

About the Authors
 
xv
About the Technical Reviewers
 
xvii
Acknowledgments
 
xix
Chapter 1
Machine Learning
1
Chapter 2
Machine Learning and Knowledge Discovery
19
Chapter 3
Support Vector Machines for Classification
39
Chapter 4
Support Vector Regression
67
Chapter 5
Hidden Markov Model
81
Chapter 6
Bioinspired Computing: Swarm Intelligence
105
Chapter 7
Deep Neural Networks
127
Chapter 8
Cortical Algorithms
149
Chapter 9
Deep Learning
167
Chapter 10
Multiobjective Optimization
185
Chapter 11
Machine Learning in Action: Examples
209
Index
 
241

Book Details

Subject
Computer Science
Publisher
Apress
Published
2015
Pages
244
Edition
1
Language
English
ISBN13
9781430259893
ISBN10
1430259892
ISBN13 Digital
9781430259909
ISBN10 Digital
1430259906
PDF Size
8.2 MB
License
CC BY-NC-ND

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