Search Results - (((((((kant OR mantic) OR semantic) OR want) OR cantor) OR anne) OR shape) OR hints) algorithms.

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    Nearest-neighbor methods in learning and vision : theory and practice

    Published 2005
    Table 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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    3D Shape Analysis : Fundamentals, Theory and Applications. by Tabia, Hedi

    Published 2018
    Table 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 2004
    Table 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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    Learning Neo4j 3.x - Second Edition. by Baton, Jerome

    Published 2017
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    Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. by Camps-Valls, Gustau

    Published 2021
    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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    Algorithms for sample preparation with microfluidic lab-on-chip by Bhattacharjee, Sukanta, Bhattacharya, Bhargab B., Chakrabarty, Krishnendu

    Published 2019
    Table 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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    Romance syntax, semantics and L2 acquisition : selected papers from the 30th Linguistic Symposium on Romance Languages : Gainesville, Florida, February 2000

    Published 2001
    Table of Contents: “…ROMANCE SYNTAX, SEMANTICS AND L2 ACQUISITION -- Editorial page -- Title page -- Copyright page -- Acknowledgements -- Table of contents -- CONTRIBUTORS -- Dedication -- ROMANCE SYNTAX, SEMANTICS, AND L2 ACQUISITION -- 0. …”
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    Electronic Conference Proceeding eBook
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    Genetic and evolutionary computation : medical applications

    Published 2010
    Table of Contents: “…Potok -- Towards an alternative to magnetic resonance imaging for vocal tract shape measurement using the principles of evolution / David M. …”
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    What Is Thought? by Baum, Eric B., 1957-

    Published 2004
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    We are data : algorithms and the making of our digital selves by Cheney-Lippold, John

    Published 2017
    Table of Contents: “…-- Privacy: wanted dead or alive -- Conclusion: ghosts in the machine.…”
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