Efficient learning machines : theories, concepts, and applications for engineers and system Designers /: theories, concepts, and applications for engineers and system Designers. ([2015])

• Record Type: Book
• Further Information: Note: Mariette Awad, Rahul Khanna.
• Authors: Awad, Mariette; Khanna, Rahul, 1966-
• Contents: At a Glance; 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 DataData 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 -MeansStreaming 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 ClassificationSVM 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 RepresentationBayesian 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.
• Publisher Details: New York : Apress Open
• Publication Date: 2015
• Extent: 1 online resource, illustrations
• Subjects: 006.3/1; Computer science; Machine learning; COMPUTERS -- General; Machine learning; Computer Science; Artificial Intelligence (incl. Robotics); Computers -- Intelligence (AI) & Semantics; Artificial intelligence; Artificial intelligence; Electronic books; Electronic book
• Languages: English
• ISBNs: 9781430259909; 1430259906; 1430259892; 9781430259893
• Related ISBNs: 9781430259893
• Notes: Note: Includes bibliographical references and index.; Note: Vendor-supplied metadata.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.