Search Results - (((((((want OR wanti) OR semantic) OR win) OR cantor) OR anne) OR blaney) OR hints) algorithms.

RF Positioning : Fundamentals, Applications, and Tools. by Campos, Rafael Saraiva

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The Princeton companion to mathematics

Table of Contents: “…Algorithms ;…”
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Acquisition and understanding of process knowledge using problem solving methods by Gómez-Pérez, José Manuel

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Optimization theory and related topics : Israel mathematical conference proceedings : a workshop in memory of Dan Butnariu, January 11-14, 2010, Haifa, Israel

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Life sciences, information sciences

Table of Contents: “…Semantics --…”
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Integrated systems engineering : a postprint volume from the IFAC Conference, Baden-Baden, Germany, 27-29 September 1994

Table of Contents: “…An Optimisation-Based Algorithm for Designing Free-Form Trajectories Under Various Constraints /…”
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When life is linear : from computer graphics to bracketology by Chartier, Timothy P.

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Interactive displays : natural human interface technologies

Table of Contents: “…Gesture Detection Algorithm /…”
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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…WinMain() Function --…”
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What intelligence tests miss : the psychology of rational thought by Stanovich, Keith E., 1950-

Table of Contents: “…Inside George W. Bush's mind : hints at what IQ tests miss -- Dysrationalia : separating rationality and intelligence -- The reflective mind, the algorithmic mind, and the autonomous mind -- Cutting intelligence down to size -- Why intelligent people doing foolish things is no surprise -- The cognitive miser : ways to avoid thinking -- Framing and the cognitive miser -- Myside processing : heads I win, tails I win too! …”
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Modeling Reality : How Computers Mirror Life. by Białynicki-Birula, Iwo

Table of Contents: “…Contents; 1 From building blocks to computers: Models and modeling; 2 The game of life: A legendary cellular automaton; 3 Heads or tails: Probability of an event; 4 Galton's board: Probability and statistics; 5 Twenty questions: Probability and information; 6 Snowflakes: The evolution of dynamical systems; 7 The Lorenz butterfly: Deterministic chaos; 8 From Cantor to Mandelbrot: Self-similarity and fractals; 9 Typing monkeys: Statistical linguistics; 10 The bridges of Königsberg: Graph theory; 11 Prisoner's dilemma: Game theory; 12 Let the best man win: Genetic algorithms.…”
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Advances in Knowledge-Based and Intelligent Engineering and Information Systems by Frydman, Claudia

Table of Contents: “…Covers; Editorial advisory board; Guest editorial; Towards semantically-aided domain specific business process modeling; Topological and topical characterisation of Twitter user communities; Selection methods and diversity preservation in many-objective evolutionary algorithms; Towards a common and semantic representation of e-portfolios; Logical foundations of hierarchical model checking…”
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Understanding Artificial Intelligence. by Sabouret, Nicolas

Table of Contents: “…A Small Walk in Paris -- How a GPS Works -- Finding the Path -- It Is More Difficult Than It Looks -- The Adventurers of the Lost Graph -- So Easy! -- It's a Win-Win -- Computer Science in Three Questions -- A Clever Algorithm -- AI Is Here! …”
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Artificial neural systems : principle and practice by Lorrentz, Pierre

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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Digital journalism, drones, and automation : the language and abstractions behind the news by Dowd, Cate

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Bayesian Methods for Management and Business : Pragmatic Solutions for Real Problems. by Hahn, Eugene D.

Table of Contents: “…Chapter 4: Markov Chain Monte Carlo and Regression Models -- 4.1 Introduction to Markov Chain Monte Carlo -- 4.2 Fundamentals of MCMC -- 4.3 Gibbs Sampling -- 4.4 Gibbs Sampling and the Simple Linear Regression Model -- 4.5 In Practice: The Simple Linear Regression Model -- 4.6 The Metropolis Algorithm -- 4.7 Hastings' Extension of the Metropolis Algorithm -- 4.8 Summary -- 4.9 Exercises -- Chapter 5: Estimating Bayesian Models With WinBUGS -- 5.1 An Introduction to WinBUGS -- 5.2 In Practice: A First WinBUGS MODEL -- 5.3 In Practice: Models for the Mean in WinBUGS…”
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Navigation signal processing for GNSS software receivers by Pany, Thomas

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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn by Hackeling, Gavin

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Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios, 1951-

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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F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

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