Добірка наукової літератури з теми "Classification des motifs"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Classification des motifs".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Classification des motifs":
Meranggi, Dewa Gede Trika, Novanto Yudistira, and Yuita Arum Sari. "Batik Classification Using Convolutional Neural Network with Data Improvements." JOIV : International Journal on Informatics Visualization 6, no. 1 (March 25, 2022): 6. http://dx.doi.org/10.30630/joiv.6.1.716.
Kangkachit, Thanapat, Kitsana Waiyamai, and Philippe Lenca. "Enzyme classification using reactive motifs." International Journal of Functional Informatics and Personalised Medicine 4, no. 3/4 (2014): 243. http://dx.doi.org/10.1504/ijfipm.2014.068173.
Reddy, Ayaluri Mallikarjuna, Vakulabharanam Venkata Krishna, Lingamgunta Sumalatha, and Avuku Obulesh. "Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 965–76. http://dx.doi.org/10.2174/2213275912666190417151247.
Nepomnyashchikh, N. A. "Hagiographicals Plots and Motives on the Modern Period: On Issue of Research and Classification." Studies in Theory of Literary Plot and Narratology, no. 1 (2019): 123–38. http://dx.doi.org/10.25205/2410-7883-2019-1-123-138.
Xie, Wen-Jie, Rui-Qi Han, and Wei-Xing Zhou. "Time series classification based on triadic time series motifs." International Journal of Modern Physics B 33, no. 21 (August 20, 2019): 1950237. http://dx.doi.org/10.1142/s0217979219502370.
Petrov, A. I., C. L. Zirbel, and N. B. Leontis. "Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas." RNA 19, no. 10 (August 22, 2013): 1327–40. http://dx.doi.org/10.1261/rna.039438.113.
Cobanoglu, M. C., Y. Saygin, and U. Sezerman. "Classification of GPCRs Using Family Specific Motifs." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 6 (November 2011): 1495–508. http://dx.doi.org/10.1109/tcbb.2010.101.
Degtyarenko, K. "Bioinorganic motifs: towards functional classification of metalloproteins." Bioinformatics 16, no. 10 (October 1, 2000): 851–64. http://dx.doi.org/10.1093/bioinformatics/16.10.851.
Nguyen, Hai-Long, Wee-Keong Ng, and Yew-Kwong Woon. "Closed motifs for streaming time series classification." Knowledge and Information Systems 41, no. 1 (June 7, 2013): 101–25. http://dx.doi.org/10.1007/s10115-013-0662-6.
Kari, Rabiatuadawiyah, Mohd Azhar Samin, and Rafeah Legino. "The Flora Motif as Design Identity in Local Traditional Block Batik." Environment-Behaviour Proceedings Journal 5, SI3 (December 28, 2020): 123–27. http://dx.doi.org/10.21834/ebpj.v5isi3.2542.
Дисертації з теми "Classification des motifs":
Piipari, Matias. "Inference and classification of eukaryotic cis-regulatory motifs." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609801.
Gay, Dominique. "Calcul de motifs sous contraintes pour la classification supervisée." Phd thesis, Université de Nouvelle Calédonie, 2009. http://tel.archives-ouvertes.fr/tel-00516706.
Claudon, Nicolas. "Classification automatique des diatomées : une approche par les motifs des structures internes /." Thèse, Trois-Rivières : Université du Québec à Trois-Rivières, 2007. http://www.uqtr.ca/biblio/notice/resume/30024826R.pdf.
Claudon, Nicolas. "Classification automatique des diatomées : une approche par les motifs des structures internes." Thèse, Université du Québec à Trois-Rivières, 2007. http://depot-e.uqtr.ca/1244/1/030024826.pdf.
Pensa, Ruggero Gaetano Boulicaut Jean-François Robardet Céline. "Un Cadre générique pour la co-classification sous contraintes application à l'analyse du transcriptome /." Villeurbanne : Doc'INSA, 2007. http://docinsa.insa-lyon.fr/these/pont.php?id=pensa.
Pensa, Ruggero Gaetano. "Un Cadre générique pour la co-classification sous contraintes : application à l'analyse du transcriptome." Lyon, INSA, 2006. http://theses.insa-lyon.fr/publication/2006ISAL0078/these.pdf.
The search for interesting groups in boolean data (sets of objects described by sets of properties) has motivated the design of methods for computing global patterns (e. G. . , partitions), and extracting local patterns s(e. G. , frequent itemsets, association rules, formal concepts. This thesis concerns co-clustering, i. E. , computing bi-partitions (coupled partitions on both dimensions). When using available co-clustering algorithms, the user can hardly exploit his/her domain knowledge since he/she has limited possibilities for setting just a few parameters. On the other hand, classical local pattern mining techniques usually provide huge collections of patterns that are hard to evaluate and interpret. We have designed a new co-clustering framework which computes a bi-partition by starting from collections of patterns that capture locally strong associations (e. G. , formal concepts, delta-bi-set that are a form of fault-tolerant patterns). The idea is that the available information about the local patterns can be exploited to build a relevant global pattern. It becomes possible to consider the declarative specification of constraints on the bi-partitions (e. G. , user-defined requirements about the shape of clusters) and to use such constraints at the local pattern mining step and then during the co-clustering phase. As such, our proposal is a contribution to the recent domain of constraint-based clustering. A dual approach consists in using local patterns to interpret bi-partitions. We propose a method for bi-cluster characterization by means of local patterns and their associated interestingness measures. The application of our methods to a gene expression data analysis scenario has illustrated the added-value of our proposal to give rise to plausible biological hypothesis
Gosselin, Stéphane. "Recherche de motifs fréquents dans une base de cartes combinatoires." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00838571.
Grunert, Steffen. "Strukturelles und funktionelles Verständnis von Membranproteinen im Kontext sequenzmotivbasierter Methoden." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-229383.
The present work was written as part of a cooperative doctorate between the TU Dresden and the University of Applied Sciences Mittweida. In the doctoral thesis, novel, computer-oriented approaches for the analysis of membrane proteins are presented. Membrane proteins are essential for many cellular processes and are important targets for a wide range of pharmaceuticals. Their sequences provide valuable and partly not yet decoded information about their three-dimensional structure and functional characteristics. The analysis of membrane proteins is an important part for the understanding of complex biological processes in the context of proteomics and genomics. Research of membrane proteins revealed a large number of short, distinct sequence motifs. The motifs found so far support the understanding of the folded protein in the Membrane environment. In this dissertation, in three different approaches it is shown how the output of sequence motif-based methods can support the understanding of structural and functional properties of membrane proteins. In general, the junction of proteomic and mutagenic information is intensified. Last but not least, the results of this work are made available for the planning of in vitro experiments as well as for further works in the field of membrane Protein analysis
Salah, Saber. "Parallel itemset mining in massively distributed environments." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT297/document.
Le volume des données ne cesse de croître. À tel point qu'on parle aujourd'hui de "Big Data". La principale raison se trouve dans les progrès des outils informatique qui ont offert une grande flexibilité pour produire, mais aussi pour stocker des quantités toujours plus grandes.à l'extraction de motifs : les motifs fréquents, et les motifs informatifs (i.e., de forte entropie)
De, Clercq Charles. "Vers une classification des décompositions motiviques d'espaces homogènes." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2011. http://tel.archives-ouvertes.fr/tel-00653272.
Книги з теми "Classification des motifs":
Weitlaner-Johnson, Irmgard. Mexican Indian folk designs: 252 motifs from textiles. New York: Dover Publications, 1993.
Haboucha, Reginetta. Types and motifs of the Judeo-Spanish folktales. New York: Garland, 1992.
Hayao, Kawai. The Japanese psyche: Major motifs in fairy tales of Japan. Woodstock, Conn: Spring, 1996.
Wilbert, Johannes. In their own words: Introduction, concordance of new motifs, and bibliography. Cambridge, Mass: Harvard University, Center for the Study of World Religions, 1992.
Hayao, Kawai. The Japanese psyche: Major motifs in the fairy tales of Japan. Dallas, Tex: Spring Publications, 1988.
Ashliman, D. L. A guide to folktales in the English language: Based on the Aarne-Thompson classification system. New York: Greenwood Press, 1987.
Kerbelytė, Bronislava. Tipy narodnykh skazok: Strukturno-semanticheskai︠a︡ klassifikat︠s︡ii︠a︡ litovskikh narodnykh skazok. Moskva: RGGU, 2005.
Powischer, Walter J. Die Thematik und Systematik der Motive auf gewebten und geknüpften textilen Arbeiten in der Belutsch-Tradition =: The subject matter and classification of motifs on pile- and flat-woven textiles in the Baluch tradition. Wien: W.J. Powischer, 2008.
Tatum, James C. A motif-index of Luis Rosado Vega's Mayan legends. Helsinki: Suomalainen Tiedeakatemia, 2000.
Goldberg, Harriet. Motif-index of medieval Spanish folk narratives. Tempe, Ariz: Medieval & Renaissance Texts & Studies, 1998.
Частини книг з теми "Classification des motifs":
Furfaro, Angelo, Maria Carmela Groccia, and Simona E. Rombo. "Image Classification Based on 2D Feature Motifs." In Flexible Query Answering Systems, 340–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40769-7_30.
Maletzke, André Gustavo, Huei Diana Lee, Gustavo Enrique, Almeida Prado Alves Batista, Cláudio Saddy Rodrigues Coy, João José Fagundes, and Wu Feng Chung. "Time Series Classification with Motifs and Characteristics." In Soft Computing for Business Intelligence, 125–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53737-0_8.
Gendron, Patrick, Daniel Gautheret, and Francois Major. "Structural Ribonucleic Acid Motifs Identification and Classification." In High Performance Computing Systems and Applications, 323–31. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5611-4_31.
Tohid, Suhaimi, Rafeah Legino, Ruzaika Omar Basaree, Ponirin Amin, and Rahman Amin. "Classification Design Motifs of Traditional Malay Wood Carving." In Proceedings of the International Symposium on Research of Arts, Design and Humanities (ISRADH 2014), 55–63. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-530-3_6.
Orengo, C., J. Thornton, L. Holm, and C. Sander. "Protein folds and motifs: representation, comparison and classification." In International Tables for Crystallography, 575–78. Chester, England: International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000714.
Mikolajczack, Jérôme, Gérard Ramstein, and Yannick Jacques. "SVM-Based Classification of Distant Proteins Using Hierarchical Motifs." In Lecture Notes in Computer Science, 25–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_4.
Schuchhardt, Johannes, Gisbert Schneider, Joachim Reichelt, Dietmar Schomburg, and Paul Wrede. "Classification of Local Protein Structural Motifs by Kohonen Networks." In Bioinformatics: From Nucleic Acids and Proteins to Cell Metabolism, 85–92. Weinheim, Germany: Wiley-VCH Verlag GmbH, 2007. http://dx.doi.org/10.1002/9783527615193.ch7.
Blekas, Konstantinos, Dimitrios I. Fotiadis, and Aristidis Likas. "Protein Sequence Classification Using Probabilistic Motifs and Neural Networks." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 702–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_84.
Kangkachit, Thanapat, and Kitsana Waiyamai. "Comprehensible Enzyme Function Classification Using Reactive Motifs with Negative Patterns." In Machine Learning and Data Mining in Pattern Recognition, 560–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_44.
Haupt, Andreas, Thomas Schultz, Mohammed Khatami, and Ngoc Tran. "Classification on Large Networks: A Quantitative Bound via Motifs and Graphons (Research)." In Advances in Mathematical Sciences, 107–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42687-3_7.
Тези доповідей конференцій з теми "Classification des motifs":
Lian, Wilson, Fabian Monrose, and John McHugh. "Traffic classification using visual motifs." In the Seventh International Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1850795.1850804.
Eser, Ercument M., Burak R. Arslan, and Ugur O. Sezerman. "Classification of cohesin family using class specific motifs." In 2013 8th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2013. http://dx.doi.org/10.1109/hibit.2013.6661687.
Arango-Argoty, G. A., J. A. Jaramillo-Garzon, S. Rothlisberger, and C. G. Castellanos-Dominguez. "Classification of unaligned sequences based on prototype motifs representation." In 2011 6th Colombian Computing Congress (CCC). IEEE, 2011. http://dx.doi.org/10.1109/colomcc.2011.5936297.
Mullane, Sean, Ruoyan Chen, Sri Vaishnavi Vemulapalli, Eli J. Draizen, Ke Wang, Cameron Mura, and Philip E. Bourne. "Machine Learning for Classification of Protein Helix Capping Motifs." In 2019 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2019. http://dx.doi.org/10.1109/sieds.2019.8735646.
Sheng, Huitao, Kishan Mehrotra, Chilukuri Mohan, and Ramesh Raina. "Classification of gene expression levels using activator and repressor motifs." In 2008 IEEE International Conference on Bioinformatics and Biomeidcine Workshops, BIBMW. IEEE, 2008. http://dx.doi.org/10.1109/bibmw.2008.4686239.
Maletzke, Andre G., Huei D. Lee, Gustavo E. A. P. A. Batista, Solange O. Rezende, Renato B. Machado, Richardson F. Voltolini, Joylan N. Maciel, and Fabiano Silva. "Time Series Classification using Motifs and Characteristics Extraction: A Case Study on ECG Databases." In Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/.2013.40.
Setyawan, Iwan, Ivanna K. Timotius, and Marchellius Kalvin. "Automatic batik motifs classification using various combinations of SIFT features moments and k-Nearest Neighbor." In 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2015. http://dx.doi.org/10.1109/iciteed.2015.7408954.
Arango-Argoty, G. A., J. A. Jaramillo-Garzon, S. Rothlisberger, and C. G. Castellanos-Dominguez. "Prediction of protein subcellular localization based on variable-length motifs detection and dissimilarity based classification." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090213.
Zhang, Su, Wei Yang, Ning Wu, Yazhu Chen, Hongtao Lu, and Zhizhou Zhang. "A Double-SVM Classification System for Single and Multiple-Subcellular Localizations of Yeast Proteins Using Sequence Motifs." In 2007 International Conference on Information Acquisition. IEEE, 2007. http://dx.doi.org/10.1109/icia.2007.4295720.
Murray, Andrew P., and Pierre M. Larochelle. "A Classification Scheme for Planar 4R, Spherical 4R, and Spatial RCCC Linkages to Facilitate Computer Animation." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/mech-5887.
Звіти організацій з теми "Classification des motifs":
Lades, M. Motion description for data compression and classification. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/8300.
Fetterer, F., and D. Gineris. Evaluating ERS-1 Ice Motion and Classification Products. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada267614.
Ravani, Bahram. Classification and Simulation of Single and Multi-Degree-of-Freedom Motions. Fort Belvoir, VA: Defense Technical Information Center, October 1989. http://dx.doi.org/10.21236/ada214925.
Li, Shuo, Yingzi (Eliza) Du, and Yi Jiang. Site Verification of Weigh-in-Motion Traffic and TIRTL Classification Data. West Lafayette, Indiana: Purdue University, 2011. http://dx.doi.org/10.5703/1288284314247.
Schwartz, Daniel F., Robert R. Bennett, Kenneth J. Graham, Thomas L. Boggs, and Alice I. Atwood. Current Efforts to Develop Alternate TB700-2" Test Protocols for the Hazard Classification of Large Rocket Motors". Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada407047.