Machine Learning

Author: Marco Gori
Publisher: Morgan Kaufmann
ISBN: 0081006705
Format: PDF, ePub, Docs
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Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book. This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included. Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner Provides in-depth coverage of unsupervised and semi-supervised learning Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex

Adaptive Learning Methods for Nonlinear System Modeling

Author: Danilo Comminiello
Publisher: Butterworth-Heinemann
ISBN: 0128129778
Format: PDF
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Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.

Artificial Intelligence Methods in the Environmental Sciences

Author: Sue Ellen Haupt
Publisher: Springer Science & Business Media
ISBN: 1402091192
Format: PDF
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How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Format: PDF, Mobi
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Vibration based Techniques For Damage Detection And Localization In Engineering Structures

Author: Nobari Ali Salehzadeh
Publisher: World Scientific
ISBN: 178634498X
Format: PDF, Mobi
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In the oil and gas industries, large companies are endeavoring to find and utilize efficient structural health monitoring methods in order to reduce maintenance costs and time. Through an examination of the vibration-based techniques, this title addresses theoretical, computational and experimental methods used within this trend. By providing comprehensive and up-to-date coverage of established and emerging processes, this book enables the reader to draw their own conclusions about the field of vibration-controlled damage detection in comparison with other available techniques. The chapters offer a balance between laboratory and practical applications, in addition to detailed case studies, strengths and weakness are drawn from a broad spectrum of information. Contents: Machine Learning Algorithms for Damage Detection (Eloi Figueiredo and Adam Santos) Data-Driven Methods for Vibration-Based Monitoring Based on the Singular Spectrum Analysis (Irina Trendafilova, David Garcia and Hussein Al-Bugharbee) Experimental Investigation of Delamination Effects on Modal Damping of a CFRP Laminate, Using a Statistical Rationalization Approach (Majid Khazaee, Ali Salehzadeh Nobari and M H Ferri Aliabadi) Problem of Detecting Damage Through Natural Frequency Changes (Gilbert-Rainer Gillich, Nuno N N Maia and Ion Cornel Mituletu) Damage Localization Based on Modal Response Measured with Shearography (J V Araújo dos Santos and H Lopes) Novel Techniques for Damage Detection Based on Mode Shape Analysis (Wieslaw Ostachowicz, Maciej Radzieński, Maosen Cao and Wei Xu) Damage Identification Based on Response Functions in Time and Frequency Domains (R P C Sampaio, T A N Silva, N M M Maia and S Zhong) Readership: Engineers, technicians, researchers working in the field of vibration-based techniques. Keywords: Structural Health Monitoring;SHM;Vibration-based SHM;Machine Learning;Time Domain Data Analysis;Frequency Domain Data Analysis;Damage IndexReview: Key Features: The 1st book to address theoretical, computational and experimental methods The book provides an up to date and comprehensive coverage of established and emerging techniques within the field of vibration-controlled damage detection Excellent balance between laboratory and practical applications Many case studies in various chapters that help the reader to identify weak and strong points of various techniques

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Author: Chris Aldrich
Publisher: Springer Science & Business Media
ISBN: 1447151852
Format: PDF, Docs
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This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.