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Cover Art
E-RESOURCE
Author Granichin, O. N. (Oleg Nikolaevich), author.

Title Randomized algorithms in automatic control and data mining / Oleg Granichin, Zeev (Vladimir) Volkovich, Dvora Toledano-Kitai.

Published Heidelberg : Springer, [2014]
©2015

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource (xxiv, 251 pages) : illustrations (some color).
Series Intelligent Systems Reference Library, 1868-4394 ; volume 67
Intelligent systems reference library ; v. 67. 1868-4394
Springer English/International eBooks 2015 - Full Set
Bibliography Includes bibliographical references and index.
Contents Preface; Contents; Introduction; Basic Notations; Part I Randomized Algorithms; 1Feedback, Averaging and Randomization in Control and Data Mining; 1.1Feedback; 1.1.1Information and Control; 1.1.2Signals and Data Sizes; 1.1.3Observations with Noise; 1.2Averaging; 1.2.1Data Averaging ; 1.2.2Averaging in Stochastic Control; 1.3Efficiency of Closed-Loop Strategies under Uncertainty; 1.4Estimation under Arbitrary Noise; 1.4.1Can Smart Estimates Be Obtained?; 1.4.2Randomized and Bayesian Approaches; 1.5Randomization for Reducing Computational Complexity ; 1.6Quantum Computing; 2Historical Overview.
2.1Game Theory2.2Monte Carlo Method, Random Search; 2.2.1Random Search, Simulating Annealing, Genetic Algorithms; 2.2.2Probabilistic Methods in a Control Syntheses; 2.3Estimation and Filtering under Arbitrary External Noises; 2.3.1Randomized Stochastic Approximation; 2.3.2Linear Regression and Filtering; 2.3.3Compressive Sensing; 2.3.4Randomized Control Strategies; 2.4Data Mining and Machine Learning; 2.4.1Clustering; 2.4.2Cluster Validation; Part II Randomization in Estimation, Identification and Filtering Problems; 3Randomized Stochastic Approximation; 3.1 Mean-Risk Optimization.
3.2Exciting Testing Perturbation as Randomization3.3Convergence of Estimates; 3.4Fastest Rate of Convergence; 3.5Tracking; 3.6Algorithm Implementation and Quantum Computing; 3.7Applications; 3.7.1Optimization of a Server Processing Queue; 3.7.2Load Balancing; 3.7.3UAV Soaring; 4Linear Models; 4.1Linear Regression and Filtering under Arbitrary External Noise; 4.1.1Linear Regression; 4.1.2Application in Photoemission Experiments; 4.1.3Moving Average; 4.1.4Filtering; 4.2Random Projections and Sparsity; 4.2.1Compressed (Spars) Signals; 4.2.2Transforming Coding; 4.2.3Compressive Sensing.
4.2.4Universal Measurement Matrix: Random Projections4.2.5Reconstruction Algorithms through 1-Optimization and Others; 4.2.6Some Applications; 5Randomized Control Strategies; 5.1Preliminary Example; 5.2Problem Statement; 5.3Control Actions with Randomized Test Signals; 5.4Assumptions and Model Reparameterization; 5.5Stochastic Approximation Algorithm; 5.6Procedure for Constructing Confidence Regions; 5.7Combined Procedure; 5.8Randomized Control for Small UAV under Unknown Arbitrary Wind Disturbances; Part III Data Mining; 6Clustering; 6.1Partition into k Clusters; 6.2k-Means Clustering.
6.2.1Randomized Iterative k-Means6.2.2Example; 6.3Clustering and Compressive Sensing; 6.4Gaussian Mixtures and Clustering; 6.5Information Methods; 6.5.1Mixture Clustering Model -- An Information Standpoint; 6.5.2Information Bottleneck; 6.6Spectral Clustering; 6.6.1The Ng-Jordan-Weiss (NJW) Algorithm; 6.6.2sigma-Learning and Model Selection; 6.6.3Numerical Experiments; 6.6.4Application to Writing Style Determination; 7Cluster Validation ; 7.1Stability Criteria; 7.1.1External Indexes; 7.1.2The Clest Algorithm; 7.1.3The General Stability Approach; 7.2Geometrical Criteria; 7.3Information Criteria.
Summary In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a pro.
Other author Volkovich, Zeev, author.
Toledano-Kitai, Dvora, author.
SpringerLink issuing body.
Subject Computer algorithms.
Automatic control.
Data mining.
Electronic books.
ISBN 9783642547867
3642547869
9783642547850
Standard Number 10.1007/978-3-642-54786-7