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Libros sobre el tema "Learning on graphs"

Autor: Grafiati
Publicado: 10 de marzo de 2023
Última modificación: 11 de marzo de 2023

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1

Irniger, Christophe-André Mario. Graph matching: Filtering databases of graphs using machine learning techniques. Berlin: AKA, 2005.

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2

Let's eat lunch: Learning about picture graphs. New York: Rosen Classroom Books & Materials, 2004.

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3

Swan, Malcolm. Learning the language of functions and graphs. [Nottingham]: [Shell Centre for Mathematical Education, University of Nottingham], 1988.

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4

On the trail with Lewis and Clark: Learning to use line graphs. New York: Rosen Pub. Group, 2004.

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5

Fung, P. W. Designing an intelligent case-based learning environment in physics with conceptual graphs. Manchester: UMIST, 1996.

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6

Bayesian networks and decision graphs. New York: Springer, 2001.

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7

Sudre, Carole H., Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput et al., eds. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6.

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8

Hamilton, William L. Graph Representation Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01588-5.

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9

Subramanya, Amarnag y Partha Pratim Talukdar. Graph-Based Semi-Supervised Learning. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01571-7.

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10

Zhang, Daoqiang, Luping Zhou, Biao Jie y Mingxia Liu, eds. Graph Learning in Medical Imaging. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35817-4.

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11

Shi, Chuan, Xiao Wang y Philip S. Yu. Heterogeneous Graph Representation Learning and Applications. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6166-2.

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12

Guan, Weili, Xuemeng Song, Xiaojun Chang y Liqiang Nie. Graph Learning for Fashion Compatibility Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18817-6.

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13

Wendy, Chang, ed. Multiple intelligences activities: Grades 5-8. Huntington Beach, CA: Teacher Created Materials, Inc., 1996.

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14

Sally, Bowman, Chertok Michael, Corbett Beverly y Computer Learning Foundation, eds. Computer Learning Foundation: Learning together, grades K-5 lesson plans. Palo Alto, CA: Computer Learning Foundation, 1989.

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15

Sally, Bowman, Chertok Michael, Corbett Beverly y Computer Learning Foundation, eds. Computer Learning Foundation: Learning together, grades 9-12 lesson plans. Palo Alto, CA: Computer Learning Foundation, 1989.

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16

Smith, Bigham Vicki, Bowman Sally, Corbett Beverly y Computer Learning Foundation, eds. Computer Learning Foundation: Learning together, grades 6-8 lesson plans. Palo Alto, CA: Computer Learning Foundation, 1989.

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17

Kolodziej, Nancy J. Learning station models for middle grades. Westerville, Ohio: National Middle School Association, 2010.

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18

Kolodziej, Nancy J. Learning station models for middle grades. Westerville, Ohio: National Middle School Association, 2010.

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19

Ma, Yao y Jiliang Tang. Deep Learning on Graphs. University of Cambridge ESOL Examinations, 2021.

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20

Ma, Yao y Jiliang Tang. Deep Learning on Graphs. University of Cambridge ESOL Examinations, 2021.

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21

Engebrecht, Victor M. Learning about graphs, tables, and other visual aids. Hayes School Pub. Co, 1998.

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22

Graham, Roland y National Geographic Learning Staff. Let's Vote! Learning to Use Simple Bar Graphs. National Geographic School Publishing, Incorporated, 2010.

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23

Staff, Scholastic. Scholastic Learning Express Level 3: Fractions and Graphs. Scholastic, Incorporated, 2013.

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24

Bowen, Tyrone y Karen cooke. Learning Decimals : : Convert Fractions to Decimals, Using Graphs. Independently Published, 2018.

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25

Nielsen, Thomas Dyhre y Finn VERNER JENSEN. Bayesian Networks and Decision Graphs. Springer, 2007.

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26

Nielsen, Thomas Dyhre y Finn VERNER JENSEN. Bayesian Networks and Decision Graphs. Springer New York, 2013.

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27

Drooyan, Franklin /. Modeling, Functions,and Graphs-algebra for College Students-thomson Learning (thomson learning -custom publishing). brooks / cole publishing, 2000.

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28

Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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29

Nebsm y Linda Carr. Using Graphs (Super Series: Open Learning for Supervisory Management. Tech). Elsevier Science Publishing Company, 1986.

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30

Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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31

Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley, 2013.

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32

Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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33

Aggarwal, Manasvi y M. N. Murty. Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs. Springer Singapore Pte. Limited, 2020.

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34

Tuttle, Harry Grover. Learning and Technology Assessments for Administrators with Spreadsheet and Graphs CD. Epilog Visions, 2004.

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35

Thomas, D. L. Astro Organizer: Easy Learning Reference Guide for Astrological Charts and Graphs. Astrographix, 1992.

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36

Nielsen, Thomas D. y Finn V. Jensen. Bayesian Networks and Decision Graphs. Springer New York, 2010.

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37

Linguistics, Association for Computational. Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing. Association for Computational Linguistics, 2022.

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38

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
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39

Bayesian Networks and Decision Graphs (Information Science and Statistics). Springer, 2007.

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40

Borgelt, Christian, Matthias Steinbrecher y Rudolf R. Kruse. Graphical Models: Representations for Learning, Reasoning and Data Mining. Wiley & Sons, Limited, John, 2009.

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41

Borgelt, Christian, Matthias Steinbrecher y Rudolf R. Kruse. Graphical Models: Representations for Learning, Reasoning and Data Mining. Wiley & Sons, Incorporated, John, 2009.

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42

Swiss, Tracy. Learn How to Read Crochet Patterns, Charts, Symbols and Graphs: Increase Your Crochets Skills by Learning the Fundamentals of Patterns. Independently Published, 2020.

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43

Hamilton, William L. Graph Representation Learning. Morgan & Claypool Publishers, 2020.

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44

William L. William L. Hamilton. Graph Representation Learning. Springer International Publishing AG, 2020.

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45

Hamilton, William L. Graph Representation Learning. Morgan & Claypool Publishers, 2020.

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46

Hamilton, William L. Graph Representation Learning. Morgan & Claypool Publishers, 2020.

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47

Nego, Alessandro. Graph-Powered Machine Learning. Manning Publications Company, 2020.

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48

Negro, Alessandro. Graph-Powered Machine Learning. Manning Publications Co. LLC, 2021.

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49

Liu, Feng, Yu Zhang, Jordi Solé-Casals, Islem Rekik y Yehia Massoud, eds. Graph Learning for Brain Imaging. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-83250-134-4.

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50

Subramanya, Amarnag y Partha Pratim Talukdar. Graph-Based Semi-Supervised Learning. Morgan & Claypool Publishers, 2014.

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