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  • Kalman Filtering Under Information Theoretic Criteria

    Series series Mathematics and Statistics (R0)
    This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. ... Leer más

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  • Pattern Recognition

    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 ... Leer más

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  • Regularized System Identification

    Learning Dynamic Models from Data

    Series 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 ... Leer más

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  • Hands-On Mathematics for Deep Learning

    Build a solid mathematical foundation for training efficient deep neural networks

    de Jay Dawani ...
    A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, ... Leer más

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  • Probabilistic Machine Learning

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    Series 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 ... Leer más

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  • Handbook of Blind Source Separation

    Independent Component Analysis and Applications

    Edición de Pierre Comon, Christian Jutten ...
    Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and ... Leer más

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

    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 ... Leer más

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  • Handbook of Monte Carlo Methods

    Series Libro 706 - Wiley Series in Probability and Statistics
    A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applicationsMore and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the ... Leer más

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  • Bayesian Reasoning and Machine Learning

    de David Barber ...
    Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to ... Leer más

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  • Core Concepts in Data Analysis: Summarization, Correlation and Visualization

    de Boris Mirkin ...
    Series series Computer Science (R0)
    Core Concepts in Data Analysis: Summarization, Correlation and Visualizationprovides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule).Boris Mirkin takes an unconventional ... Leer más

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  • Optimization in Engineering Sciences

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    The purpose of this book is to present the main methods of static and dynamic optimization. It has been written within the framework of the European Union project – ERRIC (Empowering Romanian Research on Intelligent Information Technologies), funded by the EU’s FP7 Research Potential program and developed in cooperation between French and Romanian teaching researchers.Through the principles of ... Leer más

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  • Analysis and Synthesis of Fault-Tolerant Control Systems

    In recent years, control systems have become more sophisticated in order to meet increased performance and safety requirements for modern technological systems. Engineers are becoming more aware that conventional feedback control design for a complex system may result in unsatisfactory performance, or even instability, in the event of malfunctions in actuators, sensors or other system components. ... Leer más

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