Search Results - ((((((kant OR caykent) OR art) OR cantor) OR anne) OR walted) OR hints) algorithms.

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    The Princeton companion to mathematics

    Published 2008
    Table of Contents: “…Algorithms ;…”
    Full text (MFA users only)
    Electronic eBook
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    Safety, reliability, and applications of emerging intelligent control technologies : a postprint volume from the IFAC workshop, Hong Kong, 12-14 December 1994

    Published 1995
    Table of Contents: “…Fuzzy Control of Water Pressure Using Genetic Algorithm /…”
    Full text (MFA users only)
    Electronic Conference Proceeding eBook
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    Multimodal transport systems

    Published 2014
    Table of Contents: “…State of the art --…”
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    Electronic eBook
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    Security of self-organizing networks : MANET, WSN, WMN, VANET

    Published 2011
    Table of Contents: “…Other Online and Adaptive Algorithms --…”
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    Electronic eBook
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    Advances in wireless communications

    Published 2002
    Full text (MFA users only)
    Electronic eBook
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    Interactive displays : natural human interface technologies

    Published 2015
    Table of Contents: “…Current State of the Art /…”
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    Electronic eBook
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    Artificial neural systems : principle and practice by Lorrentz, Pierre

    Published 2015
    Table of Contents: “…INTRODUCTIONDENSITY BASED ALGORITHMS: CLUSTERING ALGORITHMS; NATURE-BASED ALGORITHMS; Evolutionary Algorithm and Programming ; Genetic Algorithm; GA Operators; APPLICATIONS OF GENETIC ALGORITHM; NETWORK METHOD: EDGES AND NODES; MULTI-LAYERED PERCEPTRON; REAL-TIME APPLICATIONS OF STATE-OF-THE-ART ANN SYSTEMS; DEFINITION OF ARTIFICIAL NEURAL NETWORKS (ANN); Intelligence; An Artificial Neural Network (ANN) system; PERFORMANCE MEASURES; Receiver's Operating Characteristics (ROC); Hypothesis Testing; Chi-squared (Goodness-of-fit) Test; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES…”
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    Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios, 1951-

    Published 2015
    Table of Contents: “…Probability and stochastic processes -- Learning in parametric modeling: basic concepts and directions -- Mean-square error linear estimation -- Stochastic gradient descent: the LMS algorithm -- The least-squares family -- Classification: a tour of the classics -- Parameter learning: a convex analytic path -- Sparsity-aware learning: concepts and theoretical foundations -- Sparcity-aware learning: algorithms and applications -- Learning in reproducing Kernel Hilbert spaces -- Bayesian learning: inference and the EM alogrithm -- Bayesian learning: approximate inference and nonparametric models -- Monte Carlo methods -- Probabilistic graphical models: Part I -- Probabilistic graphical models: Part II -- Particle filtering -- Neural networks and deep learning -- Dimensionality reduction -- Appendix A LInear algebra -- Appendix B Probability theory and statistics -- Appendix C Hints on constrained optimization.…”
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