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.
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 High-Dimensional Data and High-Speed Data Streams
- Mining Sequence Data and Time Series Data
- Mining Complex Knowledge from Complex Data
- Distributed Data Mining and Mining Multi-Agent Data
- Data Mining Process-Related Problems
- Security, Privacy, and Data Integrity
- Dealing with Nonstatic, Unbalanced, and Cost-Sensitive 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
- Weighted-Sum Approach
- Vector-Evaluated 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 Envelope-Based Selection Algorithm
- Pareto Envelope-Based 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
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