Search Results - (((((((ant OR wantsi) OR semantic) OR wint) OR cantor) OR anne) OR line) OR hints) algorithms.

Semantic matchmaking with nonmonotonic description logics by Grimm, Stephan

Table of Contents: “…Nonmonotonic Reasoning in the Semantic WebNonmonotonic Description Logics; Autoepistemic Description Logics; Formal Semantics for Autoepistemic DLs; Reasoning with Epistemic Operators; Circumscriptive Description Logics; Formal Semantics for Circumscriptive DLs; Reasoning with Circumscribed Knowledge Bases; Terminological Defaults; Discussion; A Reasoning Algorithm for Circumscriptive Description Logics; Principle Idea of a Tableaux Extension; Extending DL Tableaux Calculi by Preference Clashes; Restrictions on the Formalism; Deciding Circumscriptive ALCO…”
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Artificial transmission lines for RF and microwave applications by Martín, Ferran, 1965-

Table of Contents: “…Fundamentals of planar transmission lines. 1.1. Planar transmission lines, distributed circuits and artificial transmission lines ; 1.2. …”
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Pattern Recognition. by Theodoridis, Sergios

Table of Contents: “…Chapter 13 Clustering Algorithms II: Hierarchical AlgorithmsChapter 14 Clustering Algorithms III: Schemes Based on Function Optimization; Chapter 15 Clustering Algorithms IV; Chapter 16 Cluster Validity; Appendix A Hints from Probability and Statistics; Appendix B Linear Algebra Basics; Appendix C Cost Function Optimization; Appendix D Basic Definitions from Linear Systems Theory; Index.…”
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Graph algorithms and applications 5

Table of Contents: “…Drawing graphs on two and three lines / Sabine Cornelsen, Thomas Schank and Dorothea Wagner, communicated by Xin He. …”
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Graph algorithms and applications 3

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Graph algorithms and applications 4

Table of Contents: “…Statistical analysis of algorithms: a case study of market-clearing mechanisms in the power industry / Chris Barrett [and others], communicated by Dorothea Wagner. …”
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Data management in the semantic web

Table of Contents: “…DATA MANAGEMENT IN THE SEMANTIC WEB ; DATA MANAGEMENT IN THE SEMANTIC WEB ; CONTENTS ; PREFACE ; INTERPRETATIONS OF THE WEB OF DATA; Abstract; 1. …”
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Metaheuristic optimization for the design of automatic control laws by Sandou, Guillaume

Table of Contents: “…Ant colony optimization…”
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Optimization of Computer Networks : Modeling and Algorithms: a Hands-On Approach. by Pavón Mariño, Pablo

Table of Contents: “…7.1 Introduction -- 7.2 Node Location Problems -- 7.3 Full Topology Design Problems -- 7.4 Multilayer Network Design -- 7.5 Notes and Sources -- 7.6 Exercises -- References -- Part Two: Algorithms -- Chapter 8: Gradient Algorithms in Network Design -- 8.1 Introduction -- 8.2 Convergence Rates -- 8.3 Projected Gradient Methods -- 8.4 Asynchronous and Distributed Algorithm Implementations -- 8.5 Non-Smooth Functions -- 8.6 Stochastic Gradient Methods -- 8.7 Stopping Criteria -- 8.8 Algorithm Design Hints -- 8.9 Notes and Sources -- 8.10 Exercises -- References…”
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Iterative Algorithms For Multilayer Optimizing Control. by Brdys, Mietek A.

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Learning OWL class expressions by Lehmann, Jens, 1982-

Table of Contents: “…Refinement Operator Based OWL Learning Algorithms; OCEL (OWL Class Expression Learner); Redundancy Elimination; Creating a Full Learning Algorithm; ELTL (EL Tree Learner)…”
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Natural language processing : semantic aspects by Kapetanios, Epaminondas, Tatar, Doina, 1943-, Sacarea, Christian

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Handbook of mathematical formulas by Bartsch, Hans-Jochen

Table of Contents: “…Definite integral; 7.6· Line integral; 7.7. Multiple integrals; Chapter 8. …”
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Network and discrete location : models, algorithms, and applications by Daskin, Mark S., 1952-

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Learning Neo4j 3.x - Second Edition. by Baton, Jerome

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Computer and machine vision : theory, algorithms, practicalities by Davies, E. R. (E. Roy)

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Non-line-of-sight radar by Watson, Brian C., Guerci, J. R.

Table of Contents: “…5.2 Diffraction -- 5.2.1 KED -- 5.2.2 UTD -- 5.3 SEKE -- 5.4 Radar Simulations Using Elliptical C -- 5.5 Commercial Software -- References -- 6 Computing Hardware Acceleration Strategi -- 6.1 GPU Computational Model -- 6.2 GPU Programming -- 6.3 FPGA -- 6.4 Ray-Tracing Line-of-Sight Algorithms -- 6.4.1 Shapes and Ray Intersections -- 6.4.2 Intersection Acceleration Algorith -- 6.5 GPU Ray-Tracing Example -- 6.6 FPGA Ray Tracing -- References -- About the Authors -- Index.…”
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Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. by Camps-Valls, Gustau

Table of Contents: “…6.1.4 Evaluation Metrics -- 6.1.4.1 Precision-Recall Curve -- 6.1.4.2 Average Precision and Mean Average Precision -- 6.1.5 Applications -- 6.2 Preliminaries on Object Detection with Deep Models -- 6.2.1 Two-stage Algorithms -- 6.2.1.1 R-CNNs -- 6.2.1.2 R-FCN -- 6.2.2 One-stage Algorithms -- 6.2.2.1 YOLO -- 6.2.2.2 SSD -- 6.3 Object Detection in Optical RS Images -- 6.3.1 Related Works -- 6.3.1.1 Scale Variance -- 6.3.1.2 Orientation Variance -- 6.3.1.3 Oriented Object Detection -- 6.3.1.4 Detecting in Large-size Images -- 6.3.2 Datasets and Benchmark -- 6.3.2.1 DOTA -- 6.3.2.2 VisDrone…”
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Uberland : how algorithms are rewriting the rules of work by Rosenblat, Alex, 1987-

Table of Contents: “…Introduction : using an app to go to work-Uber as a symbol of the new economy -- Workers as digital pawns : how Uber uses the sharing economy to exploit everyone -- Motivations to drive : how Uber creates jobs for many at the expense of a few -- Grandiose promises : how Uber proposes entrepreneurship to the masses -- The shady middleman : how Uber plays broker to line its pockets -- Behind the curtain : how Uber rules drivers with algorithms -- In the big leagues : how Uber plays ball -- Conclusion : the new age of Uber-how technology consumption rewrote the rules of work -- Appendix 1. methodology : how I studied Uber -- Appendix 2. ridehailing beyond Uber : meet Lyft, the younger twin.…”
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Meta-heuristic and evolutionary algorithms for engineering optimization by Bozorg-Haddad, Omid, 1974-, Solgi, Mohammad, 1989-, Loaiciga, Hugo A.

Table of Contents: “…Overview of optimization -- Introduction to meta-heuristic and evolutionary algorithms -- Pattern search (PS) -- Genetic algorithm (GA) -- Simulated annealing (SA) -- Tabu search (TS) -- Ant colony optimization (ACO) -- Particle swarm optimization (PSO) -- Differential evolution (DE) -- Harmony search (HS) -- Shuffled frog-leaping algorithm (SFLA) -- Honey-bee mating optimization (HBMO) -- Invasive weed optimization (IWO) -- Central force optimization (CFO) -- Biogeography-based optimization (BBO) -- Firefly algorithm (FA) -- Gravity search algorithm (GSA) -- Bat algorithm (BA) -- Plant propagation algorithm (PPA) -- Water cycle algorithm (WCA) -- Symbiotic organisms search (SOS) -- Comprehensive evolutionary algorithm (CEA).…”
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