Search Results - (((((((kant OR win) OR mantis) OR mind) OR cantor) OR anne) OR shared) OR hints) algorithms.

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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Open innovation : new product development essentials from the PDMA

Table of Contents: “…Pricing Algorithm --…”
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Basic data analysis for time series with R by Derryberry, DeWayne R.

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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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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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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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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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Methods for Network Optimization and Parallel Derivative-free Optimization. by Olsson, Per-Magnus

Table of Contents: “…Intro -- Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgements -- Thesis Introduction -- Contents -- Part I Contributions to Network Optimization -- 1 Introduction -- 2 Related Work -- 3 The Relay Positioning Problems -- 4 Environment Representation and Discretization -- 5 Relay Positioning Algorithms for Single Target Problems -- 6 Relay Positioning for Multiple Targets -- 7 Implementation and Experimental Results -- 8 Discussion -- Bibliography -- Part II Contributions to Derivative-free Optimization -- 9 Introduction -- 10 Setting And Problem Description -- 11 Direct Search Algorithms -- 12 Model Building Algorithms -- 13 Existing Model Building Algorithms -- 14 Radial Basis Functions -- 15 Parallelism in Algorithms for Derivative-Free Optimization -- 16 Information Sharing -- 17 Control of Optimization Algorithms -- 18 Implementation Details -- 19 Testing on Synthetic Test Cases -- 20 Testing On Industrial Test Cases -- 21 Future Work -- 22 Conclusions -- A Terms -- Bibliography.…”
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A cabinet of philosophical curiosities : a collection of puzzles, oddities, riddles, and dilemmas by Sorensen, Roy A.

Table of Contents: “…Infinite Two-Minute DebateIndian Debate Tournament; Winning by Losing; Minimising Selfishness; Lawrence of Arabia Collars a Leopard; A Bridge without Pillars; Advice from Shih Teng; Thales' Shady Measurement of Pyramids; The Cowpox Transmission Problem; Kant's Gloves; An Antipodal Algorithm; The Invisibility of Function Words; Necessary Waste; The Art of the Counterexample; The Philosophy of Scale Effects; Humble Exercise; Philosophy for the Eye; Synthetic A Priori Lies; Passive A Priori Deception; Crete Revisited; Less Lucky the Second Time?…”
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Navigation signal processing for GNSS software receivers by Pany, Thomas

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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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Autonomous mobile robots and multi-robot systems : motion-planning, communication, and swarming

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Wireless Sensor Networks : Signal Processing and Communications.

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Scientific computing with multicore and accelerators

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Analytical modeling of wireless communication systems by Chiasserini, C. F. (Carla-Fabiani), Gribaudo, Marco, 1972-, Manini, Daniele

Table of Contents: “…Safety Message Broadcasting -- Modeling Information Sharing -- Cellular Networks. Multi-RAT Algorithms.…”
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Naming and reference : the link of word to object by Nelson, R. J. (Raymond John), 1917-

Table of Contents: “…Peirce: reference is inference -- 5. From mind to logic -- CHAPTER 3 Sense and Reference -- 1. …”
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Intelligent Technologies : From Theory to Applications - The New Trends in Computational Intelligence. by Kvasnicka, V.

Table of Contents: “…Neural Networks; Superconvergence Concept in Machine Learning; Application of the Parallel Population Learning Algorithm to Training Feed-forward ANN; Markovian Architectural Bias of Recurrent Neural Networks; SOFM Training Speedup; Generalized Forecasting Sigma-Pi Neural Network; Human Centered Intelligent Robots Using "Ontological Neural Network"; Trajectory Bounds of Solutions of Delayed Cellular Neural Networks Differential Systems; From Plain to Modular Topology: Automatic Modularization of Structured ANNs.…”
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