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

AI and IoT-based intelligent automation in robotics

Table of Contents: “…Towards analyzing skill transfer to robots based on semantically represented activities of humans /…”
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The cost of free shipping : Amazon in the global economy

Table of Contents: “…Struggling to Win Against Amazon --…”
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Computation and Communication Technologies

Table of Contents: “…Harmony Search Algorithm for PAPR Reduction --…”
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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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In the tradition of Ahlfors-Bers. V : the triennial Ahlfors-Bers Colloquium, May 8-11, 2008, Rutgers University, Newark, New Jersey

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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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Obstetric and intrapartum emergencies : a practical guide to management

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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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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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