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This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods  to  the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for  different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

Numerical methods of optimisation --- Operational research. Game theory --- Mathematical statistics --- Machine elements --- Machine learning. --- Mathematical optimization. --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Learning, Machine --- Artificial intelligence --- Machine theory --- Machine learning --- Bayesian statistical decision theory --- Mathematical optimization --- Machine learning - Mathematical models

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This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.

Numerical methods of optimisation --- Operational research. Game theory --- Mathematical statistics --- Machine elements

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Artificial intelligence. Robotics. Simulation. Graphics --- Pattern recognition systems --- Reconnaissance des formes (Informatique) --- 681.3*I52 --- 519.7 --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Design methodology: classifier design and evaluation; feature evaluation and selection; pattern analysis (Pattern recognition) --- Mathematical cybernetics --- Pattern recognition systems. --- 519.7 Mathematical cybernetics --- 681.3*I52 Design methodology: classifier design and evaluation; feature evaluation and selection; pattern analysis (Pattern recognition)

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Pattern recognition systems --- Pattern perception --- Reconnaissance des formes (Informatique) --- Perception de structure --- Pattern recognition systems. --- Agrotechnology and Food Sciences. Information and Communication Technology --- Information and Communication Technology (General) --- Information and Communication Technology (General).

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This second volume of a four volume set, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in communications and radar engineering. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its applicationAscertain how a topic relates to other areas and learn of the research issues yet to be resolvedQuick tutorial

Radar. --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Detectors --- Electronic systems --- Pulse techniques (Electronics) --- Radio --- Remote sensing --- Image processing. --- Biomedical engineering. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing