Search Results - (((((((ant OR kind) OR semantic) OR win) OR cantor) OR anne) OR shared) OR hints) algorithms.

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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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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Communicating process architectures 2002. by World Occam And Transputer User Group. Technical Meeting (25th: 2002: University Of Reading, U. K.)

Table of Contents: “…Cover; Title page; Contents; Preface; Programme Committee; Papers; Semantics of prialt in Handel-C"! Acceptances, Behaviours and Infinite Activity in CSPP; HCSP: Imperative State and True Concurrency; Consolidating the Agreement Problem Protocol Verification Environment; On the Complexity of Buffer Allocation in Message Passing Systems; Java PastSet -- A Structured Distributed Shared Memory System; Synchronous Active Objects Introduce CSP's Primitives in Java; Configurable Collective Communication in LAM-MPI; Cache-Affinity Scheduling for Fine Grain Multithreading.…”
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Digital journalism, drones, and automation : the language and abstractions behind the news by Dowd, Cate

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Generalized Network Design Problems : Modeling and Optimization. by Pop, Petrică C.

Table of Contents: “…3.2 An efficient transformation of the GTSP into the TSP3.3 An exact algorithm for the Generalized Traveling Salesman Problem; 3.4 Integer programming formulations of the GTSP; 3.4.1 Formulations based on the properties of Hamiltonian tours; 3.4.2 Flow based formulations; 3.4.3 A local-global formulation; 3.5 Solving the Generalized Traveling Salesman Problem; 3.5.1 Reinforcing ant colony system for solving the GTSP; 3.5.2 Computational results; 3.5.3 A hybrid heuristic approach for solving the GTSP; 3.5.4 Computational results; 3.6 The drilling problem; 3.6.1 Stigmergy and autonomous robots.…”
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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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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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Recent advances in hybrid metaheuristics for dataclustering

Table of Contents: “…Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Series Preface -- Preface -- Chapter 1 Metaheuristic Algorithms in Fuzzy Clustering -- 1.1 Introduction -- 1.2 Fuzzy Clustering -- 1.2.1 Fuzzy c-means (FCM) clustering -- 1.3 Algorithm -- 1.3.1 Selection of Cluster Centers -- 1.4 Genetic Algorithm -- 1.5 Particle Swarm Optimization -- 1.6 Ant Colony Optimization -- 1.7 Artificial Bee Colony Algorithm -- 1.8 Local Search-Based Metaheuristic Clustering Algorithms -- 1.9 Population-Based Metaheuristic Clustering Algorithms -- 1.9.1 GA-Based Fuzzy Clustering…”
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Autonomous mobile robots and multi-robot systems : motion-planning, communication, and swarming

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Advances in Computational Intelligence : Theory & Applications by Wang, Fei-Yue

Table of Contents: “…Lin and F.-Y. Wang -- 8. Ant colony algorithms: the state-of-the-art / J. …”
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Equity in Instructional Technologies by Watulak, Sarah Lohnes

Table of Contents: “…Covers; Editorial advisory board; Guest editorial; Learning to fall forward; Critical literacy in #DigitalActivism: collaborative choice and action; What kind of (digital) citizen?; Equity-oriented design in open education; Scratching the coding surface: tackling algorithms for inclusion and learning…”
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Optimization of Logistics. by Yalaoui, Alice

Table of Contents: “…2.4.1. Genetic algorithms2.4.2. Ant colonies; 2.4.3. Tabu search; 2.4.4. …”
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Stochastic Methods for Boundary Value Problems : Numerics for High-dimensional PDEs and Applications. by Sabelfeld, Karl K.

Table of Contents: “…2.4 Asymptotically unbiased estimators based on singular approximations 2.5 Integral equation of the first kind ; 3 Random walk-on-boundary algorithms for the Laplace equation ; 3.1 Newton potentials and boundary integral equations of the electrostatics.…”
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Advances in intelligent IT : active media technology 2006

Table of Contents: “…Title page; Preface; Organization; Contents; Keynotes; Active Media Technologies (AMT) from the Standpoint of the Wisdom Web; Anytime Learning and Classification for Online Applications; Domain-Driven Data Mining: Methodologies and Applications; Regular Papers; Web Service Request Transformatter; A Head-Tracker Based on the Lucas-Kanade Optical Flow Algorithm; A Fair Peer Selection Algorithm for an Ecommerce-Oriented Distributed Recommender System; Intelligent Decision Making with the Semantic Web; Relevence Assessment of Topic Ontology; Information Extraction from a Whole Web Site.…”
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Wireless Sensor Networks : Signal Processing and Communications.

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Challenges and opportunities of HealthGrids : proceedings of Healthgrid 2006

Table of Contents: “…Advancing of Russian ChemBioGrid by Bringing Data Management Tools into Collaborative EnvironmentGPS@ Bioinformatics Portal: From Network to EGEE Grid; Blast2GO Goes Grid: Developing a Grid-Enabled Prototype for Functional Genomics Analysis; Bioprofiling over Grid for eHealthcare; SARS Grid-An AG-Based Disease Management and Collaborative Platform; Knowledge Discovery on HealthGrids; A Secure Semantic Interoperability Infrastructure for Inter-Enterprise Sharing of Electronic Healthcare Records.…”
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Numerical solution of variational inequalities by adaptive finite elements by Suttmeier, Franz-Theo

Table of Contents: “…Models in elasto-plasticity -- The dual-weighted-residual method -- Extensions to stabilised schemes -- Obstacle problem -- Signorini's problem -- Strang's problem -- General concept -- Lagrangian formalism -- Obstacle problem revisited -- Variational inequalities of second kind -- Time-dependent problems -- Applications -- Iterative Algorithms -- Conclusion.…”
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Parallel computing : technology trends

Table of Contents: “…Feedback-Driven Performance and Precision Tuning for Automatic Fixed Point Exploitation -- Parallel Programming -- A GPU-CUDA Framework for Solving a Two-Dimensional Inverse Anomalous Diffusion Problem -- Parallelization Strategies for GPU-Based Ant Colony Optimization Applied to TSP -- DBCSR: A Blocked Sparse Tensor Algebra Library -- Acceleration of Hydro Poro-Elastic Damage Simulation in a Shared-Memory Environment -- BERTHA and PyBERTHA: State of the Art for Full Four-Component Dirac-Kohn-Sham Calculations -- Prediction-Based Partitions Evaluation Algorithm for Resource Allocation…”
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