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Engineering (R0) Série de Livres électroniques

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  • Belief Reliability Theory and Methodology

    par Rui Kang ...
    Traduit par Qingyuan Zhang ...
    Collections series Engineering (R0)
    This book, from the perspective of reliability science construction, proposes a new theory called BELIEF RELIABILITY theory on the basis of probability theory, uncertainty theory and chance theory. The main topics include the philosophical basis of reliability science, the principles of reliability science, the criteria of reasonable reliability metrics and the basic theoretical framework and ... En savoir plus

    $205.99 CAD

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  • Pattern Recognition

    par Konstantinos Koutroumbas, Sergios Theodoridis ...
    This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods ... En savoir plus

    $95.99 CAD

  • Machine Learning

    A Constraint-Based Approach

    par Marco Gori ...
    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 ... En savoir plus

    $70.99 CAD

  • Regularized System Identification

    Learning Dynamic Models from Data

    par Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung ...
    Collections series Engineering (R0)
    This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The ... En savoir plus

    Gratuit

  • Statistical Analysis Techniques in Particle Physics

    Fits, Density Estimation and Supervised Learning

    par Ilya Narsky, Frank C. Porter ...
    Modern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students. ... En savoir plus

    $140.99 CAD

  • Probabilistic Machine Learning

    An Introduction

    par Kevin P. Murphy ...
    Collections series Adaptive Computation and Machine Learning series
    A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra ... En savoir plus

    $127.99 CAD

  • Data-Driven Fault Detection and Reasoning for Industrial Monitoring

    par Jing Wang, Jinglin Zhou, Xiaolu Chen ...
    Collections series Engineering (R0)
    This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and ... En savoir plus

    Gratuit

  • Kernel Methods and Machine Learning

    par S. Y. Kung ...
    Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to ... En savoir plus

    $110.99 CAD

  • Machine Learning - A Journey To Deep Learning: With Exercises And Answers

    par Andreas Miroslaus Wichert, Luis Sa-couto ...
    This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives — the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning ... En savoir plus

    $151.99 CAD

  • Gmdh-methodology And Implementation In Matlab

    par Godfrey C Onwubolu ...
    Group method of data handling (GMDH) is a typical inductive modeling method built on the principles of self-organization. Since its introduction, inductive modelling has been developed to support complex systems in prediction, clusterization, system identification, as well as data mining and knowledge extraction technologies in social science, science, engineering, and medicine.This is the first ... En savoir plus

    $48.99 CAD

  • Data Analysis

    Modifié par Gérard Govaert ...
    The first part of this book is devoted to methods seeking relevant dimensions of data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, often complementary to the methods described in ... En savoir plus

    $193.99 CAD

  • Modeling and Reasoning with Bayesian Networks

    par Adnan Darwiche ...
    This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate ... En savoir plus

    $79.99 CAD