Search Results - (((((((kant OR want) OR semantic) OR win) OR cantor) OR anne) OR share) OR hints) algorithms.

The Princeton companion to mathematics

Table of Contents: “…Algorithms ;…”
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Life sciences, information sciences

Table of Contents: “…Some proteins share the same evolutionary history --…”
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Acquisition and understanding of process knowledge using problem solving methods by Gómez-Pérez, José Manuel

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Security of self-organizing networks : MANET, WSN, WMN, VANET

Table of Contents: “…Other Online and Adaptive Algorithms --…”
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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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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…WinMain() Function --…”
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Interactive displays : natural human interface technologies

Table of Contents: “…Gesture Detection Algorithm /…”
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Handbook of performability engineering

Table of Contents: “…Tampered Failure Rate Load-Sharing Systems: Status and Perspectives /…”
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Market liquidity : theory, evidence, and policy by Foucault, Thierry

Table of Contents: “…Risk-sharing Effects…”
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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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Open innovation : new product development essentials from the PDMA

Table of Contents: “…Pricing Algorithm --…”
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Learning Boost C++ libraries : solve practical programming problems using powerful, portable, and expressive libraries from Boost by Mukherjee, Arindam

Table of Contents: “…Shared ownership semanticsboost::shared_ptr and std::shared_ptr; Intrusive smart pointers -- boost::intrusive_ptr; shared_array; Managing non-memory resources using smart pointers; Self-test questions; Summary; References; Chapter 4: Working with Strings; Text processing with Boost String Algorithms library; Using Boost String Algorithms; Find algorithms; Case-conversion and trimming algorithms; The replace and erase algorithms; The split and join algorithms; Splitting text using the Boost Tokenizer library; Tokenizing based on separators…”
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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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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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Computational models of argument : proceedings of COMMA 2014

Table of Contents: “…Properties of Random VAFs and Implications for Efficient AlgorithmsResolution-Based Grounded Semantics Revisited -- Decomposing Abstract Dialectical Frameworks -- A Principled Approach to the Implementation of Argumentation Models -- Combining Paraconsistent Logic with Argumentation -- Probabilistic Argument Graphs for Argumentation Lotteries -- Enthymeme Construction in Dialogues Using Shared Knowledge -- Strategies in Dialogues: A Game-Theoretic Approach -- Argumentation Logic -- Splitting Abstract Dialectical Frameworks…”
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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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