Efficient Learning Machines : : Theories, Concepts, and Applications for Engineers and System Designers.

Efficient Learning Machines : : Theories, Concepts, and Applications for Engineers and System Designers.

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Bibliographic Details
:
Awad, Mariette.
TeilnehmendeR:
Khanna, Rahul.
Place / Publishing House:Berkeley, CA : : Apress L. P.,, 2015.
©2015.
Year of Publication:2015
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (263 pages)
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Table of Contents:
  • Intro
  • 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
  • Popular Machine Learning Algorithms
  • C4.5
  • k -Means
  • Support Vector Machines
  • Apriori
  • Estimation Maximization
  • PageRank
  • AdaBoost (Adaptive Boosting)
  • k -Nearest Neighbors
  • Naive Bayes
  • Classification and Regression Trees
  • 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
  • Knowledge Discovery
  • Classification
  • Clustering
  • Dimensionality Reduction
  • Collaborative Filtering
  • Machine Learning: Classification Algorithms
  • Logistic Regression
  • Random Forest
  • Hidden Markov Model
  • Multilayer Perceptron
  • Machine Learning: Clustering Algorithms
  • k -Means Clustering
  • Fuzzy k -Means (Fuzzy c - Means)
  • Streaming k -Means
  • Streaming Step
  • Ball K-Means Step
  • Machine Learning: Dimensionality Reduction
  • Singular Value Decomposition
  • Principal Component Analysis
  • Lanczos Algorithm
  • Initialize
  • Algorithm
  • Machine Learning: Collaborative Filtering
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
  • Alternating Least Squares with Weighted- l -Regularization
  • Machine Learning: Similarity Matrix
  • Pearson Correlation Coefficient
  • Spearman Rank Correlation Coefficient
  • Euclidean Distance.
  • Jaccard Similarity Coefficient
  • Summary
  • References
  • Chapter 3: Support Vector Machines for Classification
  • SVM from a Geometric Perspective
  • SVM Main Properties
  • Hard-Margin SVM
  • Soft-Margin SVM
  • Kernel SVM
  • Multiclass SVM
  • SVM with Imbalanced Datasets
  • Improving SVM Computational Requirements
  • Case Study of SVM for Handwriting Recognition
  • Preprocessing
  • Feature Extraction
  • Hierarchical, Three-Stage SVM
  • Experimental Results
  • Complexity Analysis
  • References
  • Chapter 4: Support Vector Regression
  • SVR Overview
  • SVR: Concepts, Mathematical Model, and Graphical Representation
  • Kernel SVR and Different Loss Functions: Mathematical Model and Graphical Representation
  • Bayesian Linear Regression
  • Asymmetrical SVR for Power Prediction: Case Study
  • References
  • Chapter 5: Hidden Markov Model
  • Discrete Markov Process
  • Definition 1
  • Definition 2
  • Definition 3
  • Introduction to the Hidden Markov Model
  • Essentials of the Hidden Markov Model
  • The Three Basic Problems of HMM
  • Solutions to the Three Basic Problems of HMM
  • Solution to Problem 1
  • Forward Algorithm
  • Backward Algorithm
  • Scaling
  • Solution to Problem 2
  • Initialization
  • Recursion
  • Termination
  • State Sequence Backtracking
  • Solution to Problem 3
  • Continuous Observation HMM
  • Multivariate Gaussian Mixture Model
  • Example: Workload Phase Recognition
  • Monitoring and Observations
  • Workload and Phase
  • Mixture Models for Phase Detection
  • Sensor Block
  • Model Reduction Block
  • Emission Block
  • Training Block
  • Parameter Estimation Block
  • Phase Prediction Model
  • State Forecasting Block
  • System Adaptation
  • References
  • Chapter 6: Bioinspired Computing: Swarm Intelligence
  • Applications
  • Evolvable Hardware
  • Bioinspired Networking
  • Datacenter Optimization
  • Bioinspired Computing Algorithms.
  • Swarm Intelligence
  • Ant Colony Optimization Algorithm
  • Particle Swarm Optimization
  • Artificial Bee Colony Algorithm
  • Bacterial Foraging Optimization Algorithm
  • Artificial Immune System
  • Distributed Management in Datacenters
  • Workload Characterization
  • Thermal Optimization
  • Load Balancing
  • Algorithm Model
  • References
  • Chapter 7: Deep Neural Networks
  • Introducting ANNs
  • Early ANN Structures
  • Classical ANN
  • ANN Training and the Backpropagation Algorithm
  • DBN Overview
  • Restricted Boltzmann Machines
  • DNN-Related Research
  • DNN Applications
  • P arallel Implementations to Speed Up DNN Training
  • Deep Networks Similar to DBN
  • References
  • Chapter 8: Cortical Algorithms
  • Cortical Algorithm Primer
  • Cortical Algorithm Structure
  • Training of Cortical Algorithms
  • Unsupervised Feedforward
  • Supervised Feedback
  • Weight Update
  • The workflow for CA training is displayed in Figure  8-4 .
  • Experimental Results
  • Modified Cortical Algorithms Applied to Arabic Spoken Digits: Case Study
  • Entropy-Based Weight Update Rule
  • Experimental Validation
  • References
  • Chapter 9: Deep Learning
  • Overview of Hierarchical Temporal Memory
  • Hierarchical Temporal Memory Generations
  • Sparse Distributed Representation
  • Algorithmic Implementation
  • Spatia l Poole r
  • Temporal Pooler
  • Related Work
  • Overview of Spiking Neural Networks
  • Hodgkin-Huxley Model
  • Integrate-and-Fire Model
  • Leaky Integrate-and-Fire Model
  • Izhikevich Model
  • Thorpe's Model
  • Information Coding in SNN
  • Learning in SNN
  • SNN Variants and Extensions
  • Evolving Spiking Neural Networks
  • Reservoir-Based Evolving Spiking Neural Networks
  • Dynamic Synaptic Evolving Spiking Neural Networks
  • Probabilistic Spiking Neural Networks
  • Conclusion
  • References
  • Chapter 10: Multiobjective Optimization
  • Formal Definition.
  • Pareto Optimality
  • Dominance Relationship
  • Performance Measure
  • Machine Learning: Evolutionary Algorithms
  • Genetic Algorithm
  • Genetic Programming
  • 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 of Solutions
  • Fitness of P Solutions
  • Clustering
  • 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
  • Viable System Modeling
  • Example 1: Workload Fingerprinting on a Compute Node
  • Phase Determination
  • Fingerprinting
  • Size Attribute
  • Phase Attribute
  • Pattern Attribute
  • Forecasting
  • Example 2: Dynamic Energy Allocation
  • Learning Process: Feature Selection
  • Learning Process: Optimization Planning
  • Learning Process: Monitoring
  • Model Training: Procedure and Evaluation
  • Example 3: System Approach to Intrusion Detection
  • Modeling Scheme
  • Observed (Emission) States
  • Hidden States
  • Intrusion Detection System Architecture
  • Profiles and System Considerations
  • Sensor Data Measurements
  • Summary
  • References
  • Index.

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