Book Description
Machine learning techniques provide costeffective 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 biologicallyinspired 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 biologicallyinspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multiobjective optimization in which solutions in realworld 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.
Table of Contents
 Cover
 Title
 Copyright
 About ApressOpen
 Dedication
 Contents at a Glance
 Contents
 About the Authors
 About the Technical Reviewers
 Acknowledgments

Chapter 1: Machine Learning
 Key Terminology
 Developing a Learning Machine
 Machine Learning Algorithms

Challenging Problems in Data Mining Research
 Scaling Up for HighDimensional Data and HighSpeed Data Streams
 Mining Sequence Data and Time Series Data
 Mining Complex Knowledge from Complex Data
 Distributed Data Mining and Mining MultiAgent Data
 Data Mining ProcessRelated Problems
 Security, Privacy, and Data Integrity
 Dealing with Nonstatic, Unbalanced, and CostSensitive Data
 Summary
 References
 Chapter 2: Machine Learning and Knowledge Discovery
 Chapter 3: Support Vector Machines for Classification
 Chapter 4: Support Vector Regression
 Chapter 5: Hidden Markov Model
 Chapter 6: Bioinspired Computing: Swarm Intelligence
 Chapter 7: Deep Neural Networks
 Chapter 8: Cortical Algorithms
 Chapter 9: Deep Learning

Chapter 10: Multiobjective Optimization
 Formal Definition
 Machine Learning: Evolutionary Algorithms

Multiobjective Optimization: An Evolutionary Approach
 WeightedSum Approach
 VectorEvaluated Genetic Algorithm
 Multiobjective Genetic Algorithm
 Niched Pareto Genetic Algorithm
 Nondominated Sorting Genetic Algorithm
 Strength Pareto Evolutionary Algorithm
 Strength Pareto Evolutionary Algorithm II
 Pareto Archived Evolutionary Strategy
 Pareto EnvelopeBased Selection Algorithm
 Pareto EnvelopeBased Selection Algorithm II
 Elitist Nondominated Sorting Genetic Algorithm
 Example: Multiobjective Optimization
 Objective Functions
 References
 Chapter 11: Machine Learning in Action: Examples
 Index
Product Information
 Title: Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers
 Author(s):
 Release date: May 2015
 Publisher(s): Apress
 ISBN: 9781430259909