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Статті в журналах з теми "Graph-based application"
FOGGIA, PASQUALE, GENNARO PERCANNELLA, CARLO SANSONE, and MARIO VENTO. "A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 843–60. http://dx.doi.org/10.1142/s0218001408006557.
Повний текст джерелаPadole, Himanshu, Shiv Dutt Joshi, and Tapan K. Gandhi. "Graph Wavelet-Based Multilevel Graph Coarsening and Its Application in Graph-CNN for Alzheimer’s Disease Detection." IEEE Access 8 (2020): 60906–17. http://dx.doi.org/10.1109/access.2020.2983590.
Повний текст джерелаGuan, Jun, Huiying Liu, Baolei Mao, and Xu Jiang. "Android Malware Detection Based on API Pairing." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 5 (October 2020): 965–70. http://dx.doi.org/10.1051/jnwpu/20203850965.
Повний текст джерелаZhu, Junxiang, Heap-Yih Chong, Hongwei Zhao, Jeremy Wu, Yi Tan, and Honglei Xu. "The Application of Graph in BIM/GIS Integration." Buildings 12, no. 12 (December 7, 2022): 2162. http://dx.doi.org/10.3390/buildings12122162.
Повний текст джерелаHOLZRICHTER, MICHAEL, and SUELY OLIVEIRA. "A GRAPH BASED DAVIDSON ALGORITHM FOR THE GRAPH PARTITIONING PROBLEM." International Journal of Foundations of Computer Science 10, no. 02 (June 1999): 225–46. http://dx.doi.org/10.1142/s0129054199000162.
Повний текст джерелаSchmalstieg, Dieter, Gerhard Reitmayr, and Gerd Hesina. "Distributed Applications for Collaborative Three-Dimensional Workspaces." Presence: Teleoperators and Virtual Environments 12, no. 1 (February 2003): 52–67. http://dx.doi.org/10.1162/105474603763835332.
Повний текст джерелаWang, Jianghan, Zhu Qu, Yihan Hu, Qiyun Ling, Jingyi Yu, and Yushan Jiang. "Diagnosis and Treatment Knowledge Graph Modeling Application Based on Chinese Medical Records." Electronics 12, no. 16 (August 11, 2023): 3412. http://dx.doi.org/10.3390/electronics12163412.
Повний текст джерелаCzerepicki, A. "Application of graph databases for transport purposes." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 3 (September 1, 2016): 457–66. http://dx.doi.org/10.1515/bpasts-2016-0051.
Повний текст джерелаZhang, Zeyue. "The Application of Graph Embedding Based on Random Walk." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 474–79. http://dx.doi.org/10.54097/hset.v16i.2624.
Повний текст джерелаAbd Rahman, Hayati, Azrina Ashaari, and Nur Azima Alya Narawi. "STORYTELLING APPLICATION BASED ON INTERACTIVE STORY GRAPH STRUCTURE (ISGS)." MALAYSIAN JOURNAL OF COMPUTING 6, no. 1 (March 9, 2021): 715. http://dx.doi.org/10.24191/mjoc.v6i1.10370.
Повний текст джерелаДисертації з теми "Graph-based application"
Ferrer, Sumsi Miquel. "Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering." Doctoral thesis, Universitat Autònoma de Barcelona, 2008. http://hdl.handle.net/10803/5788.
Повний текст джерелаEn el reconeixement estructural de patrons, els grafs han estat usats normalment per a representar objectes complexos. En el domini dels grafs, el concepte de mediana és conegut com median graph. Potencialment, té les mateixes aplicacions que el concepte de mediana per poder ser usat com a representant d'un conjunt de grafs.
Tot i la seva simple definició i les potencials aplicacions, s'ha demostrat que el seu càlcul és una tasca extremadament complexa. Tots els algorismes existents només han estat capaços de treballar amb conjunts petits de grafs, i per tant, la seva aplicació ha estat limitada en molts casos a usar dades sintètiques sense significat real. Així, tot i el seu potencial, ha restat com un concepte eminentment teòric.
L'objectiu principal d'aquesta tesi doctoral és el d'investigar a fons la teoria i l'algorísmica relacionada amb el concepte de medinan graph, amb l'objectiu final d'extendre la seva aplicabilitat i lliurar tot el seu potencial al món de les aplicacions reals. Per això, presentem nous resultats teòrics i també nous algorismes per al seu càlcul. Des d'un punt de vista teòric aquesta tesi fa dues aportacions fonamentals. Per una banda, s'introdueix el nou concepte d'spectral median graph. Per altra banda es mostra que certes de les propietats teòriques del median graph poden ser millorades sota determinades condicions. Més enllà de les aportacioncs teòriques, proposem cinc noves alternatives per al seu càlcul. La primera d'elles és una conseqüència directa del concepte d'spectral median graph. Després, basats en les millores de les propietats teòriques, presentem dues alternatives més per a la seva obtenció. Finalment, s'introdueix una nova tècnica per al càlcul del median basat en el mapeig de grafs en espais de vectors, i es proposen dos nous algorismes més.
L'avaluació experimental dels mètodes proposats utilitzant una base de dades semi-artificial (símbols gràfics) i dues amb dades reals (mollècules i pàgines web), mostra que aquests mètodes són molt més eficients que els existents. A més, per primera vegada, hem demostrat que el median graph pot ser un bon representant d'un conjunt d'objectes utilitzant grans quantitats de dades. Hem dut a terme experiments de classificació i clustering que validen aquesta hipòtesi i permeten preveure una pròspera aplicació del median graph a un bon nombre d'algorismes d'aprenentatge.
Given a set of objects, the generic concept of median is defined as the object with the smallest sum of distances to all the objects in the set. It has been often used as a good alternative to obtain a representative of the set.
In structural pattern recognition, graphs are normally used to represent structured objects. In the graph domain, the concept analogous to the median is known as the median graph. By extension, it has the same potential applications as the generic median in order to be used as the representative of a set of graphs.
Despite its simple definition and potential applications, its computation has been shown as an extremely complex task. All the existing algorithms can only deal with small sets of graphs, and its application has been constrained in most cases to the use of synthetic data with no real meaning. Thus, it has mainly remained in the box of the theoretical concepts.
The main objective of this work is to further investigate both the theory and the algorithmic underlying the concept of the median graph with the final objective to extend its applicability and bring all its potential to the world of real applications. To this end, new theory and new algorithms for its computation are reported. From a theoretical point of view, this thesis makes two main contributions. On one hand, the new concept of spectral median graph. On the other hand, we show that some of the existing theoretical properties of the median graph can be improved under some specific conditions. In addition to these theoretical contributions, we propose five new ways to compute the median graph. One of them is a direct consequence of the spectral median graph concept. In addition, we provide two new algorithms based on the new theoretical properties. Finally, we present a novel technique for the median graph computation based on graph embedding into vector spaces. With this technique two more new algorithms are presented.
The experimental evaluation of the proposed methods on one semi-artificial and two real-world datasets, representing graphical symbols, molecules and webpages, shows that these methods are much more ecient than the existing ones. In addition, we have been able to proof for the first time that the median graph can be a good representative of a class in large datasets. We have performed some classification and clustering experiments that validate this hypothesis and permit to foresee a successful application of the median graph to a variety of machine learning algorithms.
Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.
Повний текст джерелаZhu, Ruifeng. "Contribution to graph-based manifold learning with application to image categorization." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA015.
Повний текст джерелаGraph-based Manifold Learning algorithms are regarded as a powerful technique for feature extraction and dimensionality reduction in Pattern Recogniton, Computer Vision and Machine Learning fields. These algorithms utilize sample information contained in the item-item similarity and weighted matrix to reveal the intrinstic geometric structure of manifold. It exhibits the low dimensional structure in the high dimensional data. This motivates me to develop Graph-based Manifold Learning techniques on Pattern Recognition, specially, application to image categorization. The experimental datasets of thesis correspond to several categories of public image datasets such as face datasets, indoor and outdoor scene datasets, objects datasets and so on. Several approaches are proposed in this thesis: 1) A novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS) is proposed. We seek a non-linear and a linear representation of the data that can be suitable for generic learning tasks such as classification and clustering. Besides, a byproduct of the proposed embedding framework is the feature selection of the original features, where the estimated linear transformation matrix can be used for feature ranking and selection. 2) We investigate strategies and related algorithms to develop a joint graph-based embedding and an explicit feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, this chapter introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. 3) We propose the graph convolution based semi-supervised Embedding (GCSE). It provides a new perspective to non-linear data embedding research, and makes a link to signal processing on graph methods. The proposed method utilizes and exploits graphs in two ways. First, it deploys data smoothness over graphs. Second, its regression model is built on the joint use of the data and their graph in the sense that the regression model works with convolved data. The convolved data are obtained by feature propagation. 4) A flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models is introduced. We call this strategy an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently
Martineau, Maxime. "Deep learning onto graph space : application to image-based insect recognition." Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4024.
Повний текст джерелаThe goal of this thesis is to investigate insect recognition as an image-based pattern recognition problem. Although this problem has been extensively studied along the previous three decades, an element is to the best of our knowledge still to be experimented as of 2017: deep approaches. Therefore, a contribution is about determining to what extent deep convolutional neural networks (CNNs) can be applied to image-based insect recognition. Graph-based representations and methods have also been tested. Two attempts are presented: The former consists in designing a graph-perceptron classifier and the latter graph-based work in this thesis is on defining convolution on graphs to build graph convolutional neural networks. The last chapter of the thesis deals with applying most of the aforementioned methods to insect image recognition problems. Two datasets are proposed. The first one consists of lab-based images with constant background. The second one is generated by taking a ImageNet subset. This set is composed of field-based images. CNNs with transfer learning are the most successful method applied on these datasets
Kim, Pilho. "E-model event-based graph data model theory and implementation /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29608.
Повний текст джерелаCommittee Chair: Madisetti, Vijay; Committee Member: Jayant, Nikil; Committee Member: Lee, Chin-Hui; Committee Member: Ramachandran, Umakishore; Committee Member: Yalamanchili, Sudhakar. Part of the SMARTech Electronic Thesis and Dissertation Collection.
GRASSI, FRANCESCO. "Statistical and Graph-Based Signal Processing: Fundamental Results and Application to Cardiac Electrophysiology." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2710580.
Повний текст джерелаBush, Stephen J. Baker Erich J. "Automated sequence homology using empirical correlations to create graph-based networks for the elucidation of protein relationships /." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5221.
Повний текст джерелаZhu, Xiaoting. "Systematic Assessment of Structural Features-Based Graph Embedding Methods with Application to Biomedical Networks." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592394966493963.
Повний текст джерелаZhang, Yan. "Improving the efficiency of graph-based data mining with application to public health data." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Thesis/Fall2007/y_zhang_112907.pdf.
Повний текст джерелаLoureiro, Rui. "Bond graph model based on structural diagnosability and recoverability analysis : application to intelligent autonomous vehicles." Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10079/document.
Повний текст джерелаThis work deals with structural fault recoverability analysis using the bond graph model. The objective is to exploit the structural and causal properties of the bond graph tool in order to perform both diagnosis and control analysis in the presence of faults. Indeed, the bond graph tool enables to verify the structural conditions of fault recoverability not only from a control perspective but also from a diagnosis one. In this way, the set of faults that can be recovered is obtained previous to industrial implementation. In addition, a novel way to estimate the fault by a disturbing power furnished to the system, enabled to extend the results of structural fault recoverability by performing a local adaptive compensation directly from the bond graph model. Finally, the obtained structural results are validated on a redundant intelligent autonomous vehicle
Книги з теми "Graph-based application"
Xiao, Bai, Jian Cheng, and Edwin R. Hancock. Graph-based methods in computer vision: Developments and applications. Hershey: Information Science Reference, 2012.
Знайти повний текст джерелаLee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.
Знайти повний текст джерелаÖsterreichische Arbeitsgruppe für Mustererkennung. Tagung. Applications of 3D-imaging and graph-based modeling 2000: 24th Workshop of the Austrian Association for Pattern Recognition (ÖAGM/AAPR), Villach, Carinthia, Austria, May 25-26, 2000. Wien: Österreichische Computer Gesellschaft, 2000.
Знайти повний текст джерелаCoolen, A. C. C., A. Annibale, and E. S. Roberts. Applications of random graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0011.
Повний текст джерелаInvariant Object Recognition Based on Elastic Graph Matching (Frontiers in Artificial Intelligence and Applications, 86). Ios Pr Inc, 2002.
Знайти повний текст джерелаHeckel, Reiko, and Gabriele Taentzer. Graph Transformation for Software Engineers: With Applications to Model-Based Development and Domain-Specific Language Engineering. Springer International Publishing AG, 2021.
Знайти повний текст джерелаHeckel, Reiko, and Gabriele Taentzer. Graph Transformation for Software Engineers: With Applications to Model-Based Development and Domain-Specific Language Engineering. Springer, 2020.
Знайти повний текст джерелаCoolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.
Повний текст джерелаЧастини книг з теми "Graph-based application"
Symeonidou, Danai, Isabelle Sanchez, Madalina Croitoru, Pascal Neveu, Nathalie Pernelle, Fatiha Saïs, Aurelie Roland-Vialaret, Patrice Buche, Aunur-Rofiq Muljarto, and Remi Schneider. "Key Discovery for Numerical Data: Application to Oenological Practices." In Graph-Based Representation and Reasoning, 222–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_17.
Повний текст джерелаPurchase, Helen C., David Carrington, and Jo-Anne Allder. "Experimenting with Aesthetics-Based Graph Layout." In Theory and Application of Diagrams, 498–501. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44590-0_46.
Повний текст джерелаDibie, Juliette, Stéphane Dervaux, Estelle Doriot, Liliana Ibanescu, and Caroline Pénicaud. "$$[MS]^2O$$ – A Multi-scale and Multi-step Ontology for Transformation Processes: Application to Micro-Organisms." In Graph-Based Representation and Reasoning, 163–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_13.
Повний текст джерелаDeptuła, A. "Application of Game Graphs to Describe the Inverse Problem in the Designing of Mechatronic Vibrating Systems." In Graph-Based Modelling in Engineering, 189–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39020-8_14.
Повний текст джерелаYu, Yangming, Zhiyong Zha, Bo Jin, Geng Wu, and Chenxi Dong. "Graph-Based Anomaly Detection via Attention Mechanism." In Intelligent Computing Theories and Application, 401–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13870-6_33.
Повний текст джерелаAmbauen, R., S. Fischer, and Horst Bunke. "Graph Edit Distance with Node Splitting and Merging, and Its Application to Diatom Identification." In Graph Based Representations in Pattern Recognition, 95–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45028-9_9.
Повний текст джерелаDahm, Nicholas, Horst Bunke, Terry Caelli, and Yongsheng Gao. "A Unified Framework for Strengthening Topological Node Features and Its Application to Subgraph Isomorphism Detection." In Graph-Based Representations in Pattern Recognition, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38221-5_2.
Повний текст джерелаBislimovska, Bojana, Alessandro Bozzon, Marco Brambilla, and Piero Fraternali. "Graph-Based Search over Web Application Model Repositories." In Lecture Notes in Computer Science, 90–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22233-7_7.
Повний текст джерелаKazemian, Hassan, Mohammad-Hossein Amirhosseini, and Michael Phillips. "Application of Graph-Based Technique to Identity Resolution." In IFIP Advances in Information and Communication Technology, 471–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08333-4_38.
Повний текст джерелаWang, Ya, Guowen Pan, Jinwen Ma, Xiangchen Li, and Albert Zhong. "Label Similarity Based Graph Network for Badminton Activity Recognition." In Intelligent Computing Theories and Application, 557–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84522-3_46.
Повний текст джерелаТези доповідей конференцій з теми "Graph-based application"
Lim, Jiyoun, and NamKyung Lee. "Graph Feature Generation based on Scene Graph Benchmark Application on Video." In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2022. http://dx.doi.org/10.1109/ictc55196.2022.9952683.
Повний текст джерелаTang, DeQuan, and Yang Tan. "Graph-Based Bioinformatics Mining Research and Application." In 2011 Fourth International Symposium on Knowledge Acquisition and Modeling (KAM). IEEE, 2011. http://dx.doi.org/10.1109/kam.2011.83.
Повний текст джерелаXia, Chunwei, and Xin Wang. "Graph-Based Web Query Classification." In 2015 12th Web Information System and Application Conference (WISA). IEEE, 2015. http://dx.doi.org/10.1109/wisa.2015.68.
Повний текст джерелаJoo, Hanbyul, Yekeun Jeong, Olivier Duchenne, Seong-Young Ko, and In-So Kweon. "Graph-based robust shape matching for robotic application." In 2009 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2009. http://dx.doi.org/10.1109/robot.2009.5152594.
Повний текст джерелаZhi, Huilai, and Zongtian Liu. "Event Importance Analysis Based on Directed Graph." In 2008 International Symposium on Intelligent Information Technology Application Workshops. IEEE, 2008. http://dx.doi.org/10.1109/iita.workshops.2008.140.
Повний текст джерелаWang, Kai, and Danwei Chen. "Graph Structure Based Anomaly Behavior Detection." In 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/iccia-17.2017.90.
Повний текст джерелаChen, Xiaoping, Jianfeng Wang, Hong Zhang, and Qingjie Hu. "Knowledge Encapsulation and Application Based on Domain Knowledge Graph." In 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023. http://dx.doi.org/10.1109/eebda56825.2023.10090622.
Повний текст джерелаYou, Chang, Lawrence Holder, and Diane Cook. "Application of Graph-based Data Mining to Metabolic Pathways." In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.31.
Повний текст джерелаDeng, Li-Qiong, Gui-Xin Zhang, and Yuan Ren. "Image Semantic Analysis and Application Based on Knowledge Graph." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633063.
Повний текст джерелаShao, Yinning, Yukai Zhao, Hang Yu, Min Liu, and Yunlong Ma. "Graph Pooling based Human Detection Method for Industrial Application." In 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2023. http://dx.doi.org/10.1109/cscwd57460.2023.10152547.
Повний текст джерелаЗвіти організацій з теми "Graph-based application"
Patwardhan, Kedar A., Guillermo Sapiro, and Vassilios Morellas. A Graph-based Foreground Representation and Its Application in Example Based People Matching in Video (PREPRINT). Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada478409.
Повний текст джерелаWan, Wei. A New Approach to the Decomposition of Incompletely Specified Functions Based on Graph Coloring and Local Transformation and Its Application to FPGA Mapping. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6582.
Повний текст джерелаMorin, Shai, Gregory Walker, Linda Walling, and Asaph Aharoni. Identifying Arabidopsis thaliana Defense Genes to Phloem-feeding Insects. United States Department of Agriculture, February 2013. http://dx.doi.org/10.32747/2013.7699836.bard.
Повний текст джерела