Search Results - (((((((ken OR want) OR semantic) OR wind) OR cantor) OR anne) OR shape) OR hints) algorithms.
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Data management in the semantic web
Published 2011Table 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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Optimization of Computer Networks : Modeling and Algorithms: a Hands-On Approach.
Published 2016Table 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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23
Learning OWL class expressions
Published 2010Table 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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24
Introduction to programming : learn to program in Java with data structures, algorithms, and logic
Published 2018Full text (MFA users only)
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Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA
Published 2013Full text (MFA users only)
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Natural language processing : semantic aspects
Published 2014Full text (MFA users only)
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27
Nearest-neighbor methods in learning and vision : theory and practice
Published 2005Table of Contents: “…Clarkson -- Locality-sensitive hashing using stable distributions / Alexandr Andoni [and others] -- New algorithms for efficient high-dimensional nonparametric classification / Ting Liu, Andrew W. …”
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28
3D Shape Analysis : Fundamentals, Theory and Applications.
Published 2018Table of Contents: “…7.2 Problem Formulation7.3 Mathematical Tools; 7.4 Isometric Correspondence and Registration; 7.5 Nonisometric (Elastic) Correspondence and Registration; 7.6 Summary and Further Reading; 8 Semantic Correspondences; 8.1 Introduction; 8.2 Mathematical Formulation; 8.3 Graph Representation; 8.4 Energy Functions for Semantic Labeling; 8.5 Semantic Labeling; 8.6 Examples; 8.7 Summary and Further Reading; Part IV: Applications; 9 Examples of 3D Semantic Applications; 9.1 Introduction; 9.2 Semantics: Shape or Status; 9.3 Semantics: Class or Identity; 9.4 Semantics: Behavior; 9.5 Semantics: Position…”
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Applications of multi-objective evolutionary algorithms
Published 2004Table of Contents: “…Non-Dominated Sorting Evolution Strategy Algorithm (NSESA)3.3. Case Studies; 3.3.1. Shape Design of a Shielded Reactor; 3.3.2. …”
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Network and discrete location : models, algorithms, and applications
Published 2013Full text (MFA users only)
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Improved Indirect Power Control (IDPC) of Wind Energy Conversion Systems (WECS)
Published 2019Table of Contents: “…Robustness Tests7 for Mode 1, Mode 2 Mode 3 -- A-Mode 1 (Novel IDPC based on T1-FLC, T2-FLC NFC): -- B-Mode 2 (Novel IDPC based on T1-FLC, T2-FLC NFC): -- C-Mode 3 (Novel IDPC based on T1-FLC, T2-FLC NFC): -- 7. WIND-SYSTEM PERFORMANCES RECAPITULATION UNDER SIX (06) PROPOSED IDPC ALGORITHMS -- CONCLUSION -- NOTES -- REFERENCES -- General Conclusion -- 5.1. …”
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Computer and machine vision : theory, algorithms, practicalities
Published 2012Full text (MFA users only)
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Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms.
Published 2021Full text (MFA users only)
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Coherent Doppler Wind Lidars in a Turbulent Atmosphere.
Published 2013Full text (MFA users only)
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Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences.
Published 2021Table 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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Algorithms for sample preparation with microfluidic lab-on-chip
Published 2019Table of Contents: “…2.1.3 Generation of Dilution Gradients2.2 Mixing Algorithms for DMFB; 2.3 Droplet Streaming Algorithms; 2.4 Dilution and Mixing Algorithms for CFMB; 2.5 Summary; Chapter 3 -- Multiple Dilution Sample Preparation on Digital Microfluidic Biochips; 3.1 Related Work; 3.2 Tree-pruning-based Dilution Algorithm; 3.2.1 Proposed Methodology; 3.3 Experimental Results; 3.4 Conclusions; Chapter 4 -- Efficient Generation of Dilution Gradients with Digital Microfluidic Biochips; 4.1 Literature Review; 4.2 Linear Gradient; 4.3 Exponential Gradient; 4.4 Complex-shaped Gradients…”
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Fundamentals of grid computing : theory, algorithms and technologies
Published 2010Full text (MFA users only)
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