Relevant bibliographies by topics / Collective classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Collective classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Collective classification"

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Fan, Shuangfei, and Bert Huang. "Recurrent collective classification." Knowledge and Information Systems 60, no. 2 (August 30, 2018): 741–55. http://dx.doi.org/10.1007/s10115-018-1260-4.

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Monner, Derek D., and James A. Reggia. "Recurrent Neural Collective Classification." IEEE Transactions on Neural Networks and Learning Systems 24, no. 12 (December 2013): 1932–43. http://dx.doi.org/10.1109/tnnls.2013.2270376.

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Sen, Prithviraj, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. "Collective Classification in Network Data." AI Magazine 29, no. 3 (September 6, 2008): 93. http://dx.doi.org/10.1609/aimag.v29i3.2157.

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Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
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Laorden, C., B. Sanz, I. Santos, P. Galan-Garcia, and P. G. Bringas. "Collective classification for spam filtering." Logic Journal of IGPL 21, no. 4 (July 30, 2012): 540–48. http://dx.doi.org/10.1093/jigpal/jzs030.

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Wang, Liping, and Songcan Chen. "Joint representation classification for collective face recognition." Pattern Recognition 63 (March 2017): 182–92. http://dx.doi.org/10.1016/j.patcog.2016.10.004.

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Xia, Xin, David Lo, Xinyu Wang, and Xiaohu Yang. "Collective Personalized Change Classification With Multiobjective Search." IEEE Transactions on Reliability 65, no. 4 (December 2016): 1810–29. http://dx.doi.org/10.1109/tr.2016.2588139.

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Xu, Junyi, LE LI, Xin Lu, Shengze Hu, Bin Ge, Weidong Xiao, and Li Yao. "Behavior-Based Collective Classification in Sparsely Labeled Networks." IEEE Access 5 (2017): 12512–25. http://dx.doi.org/10.1109/access.2017.2723433.

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Sauvé, Méric, and Ysabel Provencher. "Défense collective des droits sociaux : des modèles d’intervention aux stratégies d’action." Reflets 23, no. 1 (August 3, 2017): 57–81. http://dx.doi.org/10.7202/1040749ar.

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Au début des années 1970, les travaux de Jack Rothman sur les pratiques en organisation communautaire ont introduit la notion de modèle de pratique en intervention communautaire. À sa classification initiale des modèles de la planification sociale, du développement local et de l’action sociale se sont ajoutées, au fil des décennies, les classifications d’autres auteures et auteurs américains et canadiens. Chacun de ces auteures et auteurs a contribué à caractériser davantage les pratiques d’intervention communautaire traditionnellement associées au domaine de l’action sociale. Les connaissances accumulées permettent aujourd’hui de proposer une classification plus fine des pratiques d’action sociale en fonction de trois stratégies : la stratégie émancipatoire, la stratégie de coopération-persuasion et la stratégie de confrontation.
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Uylaş Satı, Nur. "A Collective Learning Approach for Semi-Supervised Data Classification." Pamukkale University Journal of Engineering Sciences 24, no. 5 (2018): 864–69. http://dx.doi.org/10.5505/pajes.2017.44341.

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Krishnapriya, V., and Anuraj Mohan. "Multi-label Collective Classification Using Link Based Label Diffusion." Procedia Computer Science 143 (2018): 157–64. http://dx.doi.org/10.1016/j.procs.2018.10.372.

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Dissertations / Theses on the topic "Collective classification"

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Brophy, Jonathan. "Collective Classification of Social Network Spam." Thesis, University of Oregon, 2017. http://hdl.handle.net/1794/22625.

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Unsolicited messages affects virtually every popular social media website, and spammers have become increasingly proficient at bypassing conventional filters, prompting a stronger effort to develop new methods. First, we build an independent model using features that capture the cases where spam is obvious. Second, a relational model is built, taking advantage of the interconnected nature of users and their comments. By feeding our initial predictions from the independent model into the relational model, we can propagate and jointly infer the labels of all comments at the same time. This allows us to capture the obfuscated spam comments missed by the independent model that are only found by looking at the relational structure of the social network. The results from our experiments shows that models utilizing the underlying structure of the social network are more effective at detecting spam than ones that do not. This thesis includes previously published coauthored material.
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Teichmann, Gunter, Eva-Maria Schwartz, and Frank-Michael Dittes. "Collective Business Engineering." Technische Universität Dresden, 2011. https://tud.qucosa.de/id/qucosa%3A28064.

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Rance, Karine. "L'identité collective des nobles franccais émigrés en Allemagne (1789-1815)." Universität Leipzig, 1998. https://ul.qucosa.de/id/qucosa%3A33110.

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Schnauß, Jörg, Tom Golde, Carsten Schuldt, B. U. Sebastian Schmidt, Martin Glaser, Dan Strehle, Claus Heussinger, and Josef Alfons Käs. "Collective dynamics in a multi-filament actin bundle." Diffusion fundamentals 24 (2015) 46, S. 1, 2015. https://ul.qucosa.de/id/qucosa%3A14566.

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Neetz, Manuel. "Collective behavior of molecular motors." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-85935.

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Microtubule associated molecular motors are involved in a multitude of fundamental cellular processes such as intracellular transport and spindle positioning. During these movements multiple motor proteins often work together and are, therefore, able to exert high forces. Thus force generation and sensing are common mechanisms for controlling motor driven movement. These mechanisms play a pivotal role when motor proteins antagonize each other, e.g. to facilitate oscillations of the spindle or the nucleus. Single motor proteins have been characterized in depth over the last two decades, our understanding of the collective behavior of molecular motors remains, however, poor. Since motor proteins often cooperate while they walk along microtubules, it is necessary to describe their collective reaction to a load quantitatively in order to understand the mechanism of many motor-driven processes. I studied the antagonistic action of many molecular motors (of one kind) in a gliding geometry. For this purpose I crosslinked two microtubules in an antiparallel fashion, so that they formed \"doublets\". Then I observed the gliding motility of these antiparallel doublets and analyzed the gliding velocity with respect to the relative number of motors pulling or pushing against each other. I observed that the antiparallel doublets gliding on conventional kinesin-1 (from Drosophila melanogaster) as well as cytoplasmic dynein (from Saccharomyces cerevisae) exhibited two distinct modes of movement, slow and fast, which were well separated. Furthermore I found a bistability, meaning, that both kinds of movement, slow and fast, occurred at the same ratio of antagonizing motors. Antiparallel doublets gliding on the non-processive motor protein Ncd (the kinesin-14 from D. melanogaster) showed, however, no bistability. The collective dynamics of all three motor proteins were described with a quantitative theory based on single-motor properties. Furthermore the response of multiple dynein motors towards an external, well-defined load was measured in a gliding geometry by magnetic tweezing. Examples of multi-motor force-velocity relationships are presented and discussed. I established, furthermore, a method for counting single surface immobilized motors to guide the evaluation of the tweezing experiments.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Max-Planck-Institut für Physik komplexer Systeme, 2011. https://tud.qucosa.de/id/qucosa%3A26056.

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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous. Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change. Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Ma, Leo F. H. "Preserving Our Collective Memory: The Case of HK Magazine." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16647.

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Founded in 1991, HK Magazine was one of the major English-language publications on local affairs, social issues and entertainment listings published weekly in Hong Kong for a quarter of a century. Apart from providing local entertainment information, it also witnessed the rapid social and political changes of Hong Kong when Britain handed over sovereignty of Hong Kong to China in 1997. In July 2013, HK Magazine was sold to the South China Morning Post (SCMP) Group which was subsequently purchased by Alibaba Group in December 2015. However, the SCMP Group announced on 28 September 2016 that the magazine's final issue would be released on 7 October 2016. The closure of the magazine was viewed by many as the loss of a “fun, independent and free-thinking' publication. In response to the huge reaction from the community, the SCMP Group agreed that the content of HK Magazine would be migrated to the SCMP website before the HK Magazine website was removed. It was however revealed that the SCMP website did not preserve the full archive of HK Magazine as promised. A public appeal was launched to help preserve the collective memory of Hong Kong by archiving the back issues of the magazine. Apart from reviewing the discussion arising from the close down of HK Magazine, this paper also addresses the critical role of the library in long term preserving HK Magazine on one hand and providing access to its digital content on the other.
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Kühnemann, Matthias, Thomas Rauber, and Gudula Rünger. "Optimizing MPI Collective Communication by Orthogonal Structures." Universitätsbibliothek Chemnitz, 2007. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200701061.

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Many parallel applications from scientific computing use MPI collective communication operations to collect or distribute data. Since the execution times of these communication operations increase with the number of participating processors, scalability problems might occur. In this article, we show for different MPI implementations how the execution time of collective communication operations can be significantly improved by a restructuring based on orthogonal processor structures with two or more levels. As platform, we consider a dual Xeon cluster, a Beowulf cluster and a Cray T3E with different MPI implementations. We show that the execution time of operations like MPI Bcast or MPI Allgather can be reduced by 40% and 70% on the dual Xeon cluster and the Beowulf cluster. But also on a Cray T3E a significant improvement can be obtained by a careful selection of the processor groups. We demonstrate that the optimized communication operations can be used to reduce the execution time of data parallel implementations of complex application programs without any other change of the computation and communication structure. Furthermore, we investigate how the execution time of orthogonal realization can be modeled using runtime functions. In particular, we consider the modeling of two-phase realizations of communication operations. We present runtime functions for the modeling and verify that these runtime functions can predict the execution time both for communication operations in isolation and in the context of application programs.
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Pezet, Éric. "De la classification des emplois à la question des compétences : modélisation des relations entre gestion des ressources humaines et négociation collective." Paris, ENMP, 2001. http://www.theses.fr/2001ENMP1215.

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Klauß, Tobias. "An Interacting Particle System for Collective Migration." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1228074229228-77328.

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Kollektive Migration und Schwarmverhalten sind Beispiele für Selbstorganisation und können in verschiedenen biologischen Systemen beobachtet werden, beispielsweise in Vogel-und Fischschwärmen oder Bakterienpopulationen. Im Zentrum dieser Arbeit steht ein räumlich diskretes und zeitlich stetiges Model, welches das kollektive Migrieren von Individuen mittels eines stochastischen Vielteilchensystems (VTS) beschreibt und analysierbar macht. Das konstruierte Modell ist in keiner Klasse gut untersuchter Vielteilchensysteme enthalten, sodass der größte Teil der Arbeit der Entwicklung von Methoden zur Untersuchung des Langzeitverhaltens bestimmter VTS gewidmet ist. Eine entscheidende Rolle spielen hier Gibbs-Maße, die zu zeitlich invarianten Maßen in Beziehung gesetzt werden. Durch eine Simulationsstudie und die Analyse des Einflusses der Parameter Migrationsgeschwindigkeit, Sensitivität der Individuen und (räumliche) Dichte der Anfangsverteilung können Eigenschaften kollektiver Migration erklärt und Hypothesen für weitere Analysen aufgestellt werden
Collective migration and swarming behavior are examples of self-organization and can be observed in various biological systems, such as in flocks of birds, schools of fish or populations of bacteria. In the center of this thesis lies a stochastic interacting particle system (IPS), which is a spatially discrete model with a continuous time scale that describes collective migration and which can be treated using analytical methods. The constructed model is not contained in any class of well-understood IPS’s. The largest part of this work is used to develop methods that can be used to study the long-term behavior of certain IPS’s. Thereby Gibbs-Measures play an important role and are related to temporally invariant measures. One can explain the properties of collective migration and propose a hypothesis for further analyses by a simulation study and by analysing the parameters migration velocity, sensitivity of individuals and (spatial) density of the initial distribution
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Books on the topic "Collective classification"

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Informe sobre la negociación colectiva sectorial en la Comunidad Autónoma de Galicia. [Santiago de Compostela, Spain]: Universidade de Santiago de Compostela, 1995.

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California. Grand Jury (San Francisco). Classifications and collective bargaining. San Francisco, CA: Grand Jury, 1994.

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Bolton, Ralph. Ecuadorian truck names: A collection and classification. [Greeley, Colo.]: University of Northern Colorado, Museum of Anthropology, 1986.

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Dyková, Iva. Illustrated guide to culture collection of free-living amoebae. Praha: Academia, 2013.

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author, Franco Sheila J., and National Center for Health Statistics (U.S.), eds. 2013 NCHS urban-rural classification scheme for counties. Hyattsville, Maryland: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2014.

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Rao, K. Ramachandra. Studies on a small collection of leafhoppers (Homoptera cicadellidae) from Khasi Hills, Meghalaya. Calcutta: Zoological Survey of India, 1990.

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Turek, Vojtěch. Fossils of the world: A comprehensive practical guide to collecting and studying fossils. London: Hamlyn, 1988.

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Garon, Denise. Le système ESAR: Guide d'analyse, de classification et d'organisation d'une collection de jeux et jouets. Montréal: Éditions Asted, 2002.

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J, Franco Sheila, and National Center for Health Statistics (U.S.), eds. NCHS urban-rural classification scheme for counties. Hyattsville, Md: U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2012.

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Turek, Vojtěch. Fossils of the world: A comprehensive practical guide to collecting and studying fossils. New York: Arch Cape Press, 1990.

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Book chapters on the topic "Collective classification"

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Kajdanowicz, Tomasz, and Przemysław Kazienko. "Collective Classification." In Encyclopedia of Social Network Analysis and Mining, 144–56. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_45.

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Namata, Galileo, Prithviraj Sen, Mustafa Bilgic, and Lise Getoor. "Collective Classification." In Encyclopedia of Machine Learning and Data Mining, 1–7. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_44-1.

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Namata, Galileo, Prithviraj Sen, Mustafa Bilgic, and Lise Getoor. "Collective Classification." In Encyclopedia of Machine Learning and Data Mining, 238–42. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_44.

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Kajdanowicz, Tomasz, and Przemysław Kazienko. "Collective Classification." In Encyclopedia of Social Network Analysis and Mining, 253–65. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_45.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Collective Classification." In Encyclopedia of Machine Learning, 189–93. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_140.

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Kajdanowicz, Tomasz, and Przemyslaw Kazienko. "Collective Classification." In Encyclopedia of Social Network Analysis and Mining, 1–13. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_45-1.

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Kazienko, Przemysław, and Tomasz Kajdanowicz. "Collective Classification, Structural Features." In Encyclopedia of Social Network Analysis and Mining, 156–68. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_46.

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Kazienko, Przemysław, and Tomasz Kajdanowicz. "Collective Classification: Structural Features." In Encyclopedia of Social Network Analysis and Mining, 265–78. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_46.

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Kazienko, Przemysław, and Tomasz Kajdanowicz. "Collective Classification, Structural Features." In Encyclopedia of Social Network Analysis and Mining, 1–14. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_46-1.

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Kurnianggoro, Laksono, Wahyono, Alexander Filonenko, and Kang-Hyun Jo. "Shape Classification Using Combined Features." In Computational Collective Intelligence, 549–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67077-5_53.

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Conference papers on the topic "Collective classification"

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Ghamrawi, Nadia, and Andrew McCallum. "Collective multi-label classification." In the 14th ACM international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1099554.1099591.

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Kong, Xiangnan, Xiaoxiao Shi, and Philip S. Yu. "Multi-Label Collective Classification." In Proceedings of the 2011 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611972818.53.

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Chen, Pin-Yu, Chia-Wei Lien, Fu-Jen Chu, Pai-Shun Ting, and Shin-Ming Cheng. "Supervised Collective Classification for Crowdsourcing." In 2015 IEEE Globecom Workshops (GC Wkshps). IEEE, 2015. http://dx.doi.org/10.1109/glocomw.2015.7414077.

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Senliol, Baris, Atakan Aral, and Zehra Cataltepe. "Feature selection for collective classification." In 2009 24th International Symposium on Computer and Information Sciences (ISCIS). IEEE, 2009. http://dx.doi.org/10.1109/iscis.2009.5291828.

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Jaafor, Omar, and Babiga Birregah. "Collective classification in social networks." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110128.

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Jensen, David, Jennifer Neville, and Brian Gallagher. "Why collective inference improves relational classification." In the 2004 ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1014052.1014125.

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Duan, Yajuan, Furu Wei, Ming Zhou, and Heung-Yeung Shum. "Graph-based collective classification for tweets." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398631.

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Bilgic, Mustafa, and Lise Getoor. "Effective label acquisition for collective classification." In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401901.

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Sacca, Claudio, Michelangelo Diligenti, and Marco Gori. "Collective Classification Using Semantic Based Regularization." In 2013 12th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2013. http://dx.doi.org/10.1109/icmla.2013.57.

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Bilgic, Mustafa, Galileo Mark Namata, and Lise Getoor. "Combining Collective Classification and Link Prediction." In 2007 Seventh IEEE International Conference on Data Mining - Workshops (ICDM Workshops). IEEE, 2007. http://dx.doi.org/10.1109/icdmw.2007.35.

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Reports on the topic "Collective classification"

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Ghamrawi, Nadia, and Andrew McCallum. Collective Multi-Label Classification. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440081.

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Crane, Robert J. Evaluating MLNs for Collective Classification. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada535643.

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McDowell, Luke K., Kalyan M. Gupta, and David W. Aha. Cautious Inference in Collective Classification. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada479429.

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R.J. Garrett. CLASSIFICATION OF THE MGR SUBSURFACE WATER COLLECTION/REMOVAL SYSTEM. Office of Scientific and Technical Information (OSTI), August 1999. http://dx.doi.org/10.2172/860250.

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Schomaker, Michael E., Stanley J. Zarnoch, William A. Bechtold, David J. Latelle, William G. Burkman, and Susan M. Cox. Crown-condition classification: a guide to data collection and analysis. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2007. http://dx.doi.org/10.2737/srs-gtr-102.

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Davis, Benjamin. Applying Machine Learning to the Classification of DC-DC Converters: Real-world data collection processing & Validation. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670255.

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Evans, Julie, Kendra Sikes, and Jamie Ratchford. Vegetation classification at Lake Mead National Recreation Area, Mojave National Preserve, Castle Mountains National Monument, and Death Valley National Park: Final report (Revised with Cost Estimate). National Park Service, October 2020. http://dx.doi.org/10.36967/nrr-2279201.

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Vegetation inventory and mapping is a process to document the composition, distribution and abundance of vegetation types across the landscape. The National Park Service’s (NPS) Inventory and Monitoring (I&M) program has determined vegetation inventory and mapping to be an important resource for parks; it is one of 12 baseline inventories of natural resources to be completed for all 270 national parks within the NPS I&M program. The Mojave Desert Network Inventory & Monitoring (MOJN I&M) began its process of vegetation inventory in 2009 for four park units as follows: Lake Mead National Recreation Area (LAKE), Mojave National Preserve (MOJA), Castle Mountains National Monument (CAMO), and Death Valley National Park (DEVA). Mapping is a multi-step and multi-year process involving skills and interactions of several parties, including NPS, with a field ecology team, a classification team, and a mapping team. This process allows for compiling existing vegetation data, collecting new data to fill in gaps, and analyzing the data to develop a classification that then informs the mapping. The final products of this process include a vegetation classification, ecological descriptions and field keys of the vegetation types, and geospatial vegetation maps based on the classification. In this report, we present the narrative and results of the sampling and classification effort. In three other associated reports (Evens et al. 2020a, 2020b, 2020c) are the ecological descriptions and field keys. The resulting products of the vegetation mapping efforts are, or will be, presented in separate reports: mapping at LAKE was completed in 2016, mapping at MOJA and CAMO will be completed in 2020, and mapping at DEVA will occur in 2021. The California Native Plant Society (CNPS) and NatureServe, the classification team, have completed the vegetation classification for these four park units, with field keys and descriptions of the vegetation types developed at the alliance level per the U.S. National Vegetation Classification (USNVC). We have compiled approximately 9,000 existing and new vegetation data records into digital databases in Microsoft Access. The resulting classification and descriptions include approximately 105 alliances and landform types, and over 240 associations. CNPS also has assisted the mapping teams during map reconnaissance visits, follow-up on interpreting vegetation patterns, and general support for the geospatial vegetation maps being produced. A variety of alliances and associations occur in the four park units. Per park, the classification represents approximately 50 alliances at LAKE, 65 at MOJA and CAMO, and 85 at DEVA. Several riparian alliances or associations that are somewhat rare (ranked globally as G3) include shrublands of Pluchea sericea, meadow associations with Distichlis spicata and Juncus cooperi, and woodland associations of Salix laevigata and Prosopis pubescens along playas, streams, and springs. Other rare to somewhat rare types (G2 to G3) include shrubland stands with Eriogonum heermannii, Buddleja utahensis, Mortonia utahensis, and Salvia funerea on rocky calcareous slopes that occur sporadically in LAKE to MOJA and DEVA. Types that are globally rare (G1) include the associations of Swallenia alexandrae on sand dunes and Hecastocleis shockleyi on rocky calcareous slopes in DEVA. Two USNVC vegetation groups hold the highest number of alliances: 1) Warm Semi-Desert Shrub & Herb Dry Wash & Colluvial Slope Group (G541) has nine alliances, and 2) Mojave Mid-Elevation Mixed Desert Scrub Group (G296) has thirteen alliances. These two groups contribute significantly to the diversity of vegetation along alluvial washes and mid-elevation transition zones.
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8

Blevins, Matthew, Gregory Lyons, Carl Hart, and Michael White. Optical and acoustical measurement of ballistic noise signatures. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/39501.

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Supersonic projectiles in air generate acoustical signatures that are fundamentally related to the projectile’s shape, size, and velocity. These characteristics influence various mechanisms involved in the generation, propagation, decay, and coalescence of acoustic waves. To understand the relationships between projectile shape, size, velocity, and the physical mechanisms involved, an experimental effort captured the acoustic field produced by a range of supersonic projectiles using both conventional pressure sensors and a schlieren imaging system. The results of this ongoing project will elucidate those fundamental mechanisms, enabling more sophisticated tools for detection, classification, localization, and tracking. This paper details the experimental setup, data collection, and preliminary analysis of a series of ballistic projectiles, both idealized and currently in use by the U.S. Military.
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Neeley, Aimee, Stace E. Beaulieu, Chris Proctor, Ivona Cetinić, Joe Futrelle, Inia Soto Ramos, Heidi M. Sosik, et al. Standards and practices for reporting plankton and other particle observations from images. Woods Hole Oceanographic Institution, July 2021. http://dx.doi.org/10.1575/1912/27377.

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This technical manual guides the user through the process of creating a data table for the submission of taxonomic and morphological information for plankton and other particles from images to a repository. Guidance is provided to produce documentation that should accompany the submission of plankton and other particle data to a repository, describes data collection and processing techniques, and outlines the creation of a data file. Field names include scientificName that represents the lowest level taxonomic classification (e.g., genus if not certain of species, family if not certain of genus) and scientificNameID, the unique identifier from a reference database such as the World Register of Marine Species or AlgaeBase. The data table described here includes the field names associatedMedia, scientificName/ scientificNameID for both automated and manual identification, biovolume, area_cross_section, length_representation and width_representation. Additional steps that instruct the user on how to format their data for a submission to the Ocean Biodiversity Information System (OBIS) are also included. Examples of documentation and data files are provided for the user to follow. The documentation requirements and data table format are approved by both NASA’s SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and the National Science Foundation’s Biological and Chemical Oceanography Data Management Office (BCO-DMO).
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