UID:
almahu_9949301483102882
Format:
1 online resource (263 pages)
ISBN:
9781430259909
Note:
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.
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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.
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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.
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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.
Additional Edition:
Print version: Awad, Mariette Efficient Learning Machines Berkeley, CA : Apress L. P.,c2015 ISBN 9781430259893
Language:
English
Keywords:
Electronic books.
