Academic literature on the topic 'Ordinal information'
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Journal articles on the topic "Ordinal information"
Yager, Ronald R. "Aggregation of ordinal information." Fuzzy Optimization and Decision Making 6, no. 3 (September 13, 2007): 199–219. http://dx.doi.org/10.1007/s10700-007-9008-8.
Full textBORDOGNA, GLORIA, and GABRIELLA PASI. "AN ORDINAL INFORMATION RETRIEVAL MODEL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, supp01 (September 2001): 63–75. http://dx.doi.org/10.1142/s0218488501000995.
Full textMon-Williams, Mark, and James R. Tresilian. "Ordinal depth information from accommodation?" Ergonomics 43, no. 3 (March 2000): 391–404. http://dx.doi.org/10.1080/001401300184486.
Full textde Cooman, Gert. "Confidence relations and ordinal information." Information Sciences 104, no. 3-4 (February 1998): 241–77. http://dx.doi.org/10.1016/s0020-0255(97)00066-2.
Full textHu, QingHua, MaoZu Guo, DaRen Yu, and JinFu Liu. "Information entropy for ordinal classification." Science China Information Sciences 53, no. 6 (May 29, 2010): 1188–200. http://dx.doi.org/10.1007/s11432-010-3117-7.
Full textAlcalde-Unzu, Jorge, Ricardo Arlegi, and Miguel A. Ballester. "Uncertainty with ordinal likelihood information." Social Choice and Welfare 41, no. 2 (July 27, 2012): 397–425. http://dx.doi.org/10.1007/s00355-012-0689-8.
Full textQi, Haoliang, Sheng Li, Jianfeng Gao, Zhongyuan Han, and Xinsong Xia. "Ordinal Regression for Information Retrieval." Journal of Electronics (China) 25, no. 1 (January 2008): 120–24. http://dx.doi.org/10.1007/s11767-006-0256-5.
Full textAmigó, J. M., T. Aschenbrenner, W. Bunk, and R. Monetti. "Information-theoretical applications of ordinal patterns." IEICE Proceeding Series 2 (March 17, 2014): 182–85. http://dx.doi.org/10.15248/proc.2.182.
Full textPunkka, Antti, and Ahti Salo. "Preference Programming with incomplete ordinal information." European Journal of Operational Research 231, no. 1 (November 2013): 141–50. http://dx.doi.org/10.1016/j.ejor.2013.05.003.
Full textTian, Qing, and Songcan Chen. "A novel ordinal learning strategy: Ordinal nearest-centroid projection." Knowledge-Based Systems 88 (November 2015): 144–53. http://dx.doi.org/10.1016/j.knosys.2015.07.037.
Full textDissertations / Theses on the topic "Ordinal information"
Antoine, Sophie. "The spatial nature of ordinal information in verbal working memory." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/238833.
Full textDoctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
PREVITALI, PAOLA. "Beyond numbers: the origin of spatial associations of ordinal information." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/28332.
Full textDeza, Juan Ignacio. "Climate networks constructed by using information-theoretic measures and ordinal time-series analysis." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/286281.
Full textEl objetivo de esta tesis es la creación de redes climáticas (CN por las siglas en inglés) a partir de un conjunto global de series temporales de temperatura del aire superficial (SAT), utilizando técnicas de análisis no lineal de series temporales. Varias metodologías son aplicadas al estudio de la variabilidad climática, incluyendo la Información mutua (MI) y la información mutual condicional (CMI). El objetivo principal de esta tesis es estudiar la variabilidad climática a través del análisis de redes haciendo énfasis en los diferentes patrones espaciales y temporales del sistema climático. Una introducción a los componentes principales de este trabajo interdisciplinario se presenta en los primeros tres capítulos. La variabilidad climática y los patrones atmosféricos se introducen en el Capítulo 1, la teoría de redes en el Capítulo 2, y el análisis no lineal de series temporales, especialmente metodos en teorá de la información, en el Capítulo 3. En el Capítulo 4, la similitud estadística de las anomalías de SAT en diferentes regiones del mundo es evaluada utilizando MI. Estas redes climáticas globales son construidas a partir de series temporales de SAT promediadas a escalas de tiempo mensuales, y a partir de su representación simbólica, permitiendo un análisis de estas interdependencias en varias escalas temporales. Se identifican cambios topológicos entre las redes, como resultado de variaciones en el intervalo de construcción de losOP. Escalas intra-estacionales (unos meses), inter-estacionales (cubriendo un año) e inter-anuales (varios años), son consideradas. Se encuentra que un incremento en el espaciado de los patrones ordinales (por lo tanto, en la escala de tiempo del análisis ordinal), resulta en redes climáticas con un incremento en la conectividad en el Pacífico ecuatorial. Al contrario, el número de conexiones significativas decrece al realizar el análisis ordinal en una escala de tiempo más corta (es decir, comparando meses consecutivos). Este efecto es interpretado como una consecuencia del efecto de El Niño-Oscilación Sud (ENSO) actuando en escalas de tiempo más largas y de una mayor estocasticidad en las series temporales en escalas de tiempo más cortas. La naturaleza de las interdependencias es explorada en el Capítulo 5, utilizando datos de SAT, resultantes de un conjunto de salidas de un modelo atmosférico de circulación global (AGCM), todas forzadas por la misma temperatura de la superficie del mar (SST). Es posible separar la variabilidad atmosférica en una componente forzada y otra intrínseca a la atmósfera. De esta forma, se obtienen redes climáticas para ambos tipos de variabilidad, lo que posibilita caracterizarlas. Un análisis utilizando OP permite crear CNs para diferentes escalas temporales, y encontrar la escala de OP para la cual las diferentes redes presentan mayor conectividad. Este doble proceso de selección permitie estudiar la variabilidad de las anomalías de SAT desde un nuevo punto de vista. La conectividad de las redes climáticas así construídas permite evaluar la influencia de dos fenómenos climáticos: ENSO y la Oscilación del Atlántico Norte (NAO). Para esto, se pueden comparar las redes originales, con redes provenientes de series temporales a las que se les quitaron linealmente estos fenómenos. Un resultado clave de este análisis es que la conectividad de la red de variabilidad forzada es muy afectada por ENSO: eliminando el índice NINO3.4 (que caracteriza ENSO), se provoca una pérdida general de la conectividad en la red. El hecho de que incluso conexiones entre áreas muy alejadas del océano Pacífico ecuatorial se hayan perdido al quitar el índice, sugiere que estas regiones no están directamente conectadas sino que ambas son influenciadas por la zona dominada por ENSO, especialmente en escalas de tiempo interanuales. Por otro lado, en la red de variabilidad interna, independiente del forzado de las SST, las conexiones delMar del Labrador con el resto del mundo resultan significantemente afectadas por NAO, con un máximo en escalas intra-anuales. Aunque las conexiones no locales más fuertes resultan las forzadas por el océano, se muestra la presencia de teleconexiones asociadas con la variabilidad interna. En el Capítulo 6, una extensión natural de la metodología de construcción de redes es implementada, permitiendo inferir la dirección de las conexiones. Un índice de direccionalidad (DI), puede ser definido como la diferencia entre la CMI entre dos series temporales x(t ) e y(t ) calculada de dos formas: i) considerando la información de x(t ) contenida en τ unidades de tiempo en el pasado de y(t ) y ii) considerando la información de y(t ) contenida en τ unidades de tiempo en el pasado de x(t ). Este índice DI, se utiliza para cuantificar la dirección del flujo de información entre las series, lo que equivale a la dirección de la conexión entre los respectivos nodos de la red. Dos conjuntos de series temporales, uno promediado mensualmente y el otro promediado diariamente, son usados. Las conexiones de las redes resultantes son interpretadas en términos de fenómenos de variabilidad tropical y extratropical conocidos. Regiones específicas y relevantes son seleccionadas, la dirección neta de propagación de los patrones atmosféricos es analizada y contrastada con un test de inferencia estadística. Se encuentra que diferentes patrones de variabilidad, actúan en varias escalas de tiempo, tales como ondas sinópticas atmosféricas en los extra-trópicos o escalas de tiempo mayores en los trópicos. La dependencia de valores de DI con τ es investigada. Para la escala sinóptica (τ Ç 10 días), DI presenta una dependencia con τ, con un mínimo en los trópicos y máximos (en forma de trenes de ondas) en los extra-trópicos. Para valores mayores de τ, los links resultan ser relativamente robustos a la elección del parámetro, mostrando una conectividad alta en los trópicos y baja en los extra trópicos. El análisis demuestra la capacidad de DI de inferir la dirección neta de las interacciones climáticas, y de mejorar la compresión actual de fenómenos climáticos y de la predictabilidad climática. La red resultante está en total acuerdo con los conocimientos actuales de fenómenos climáticos, validando esta metodología para inferir, directamente de los datos, la dirección neta de las interacciones climáticas. Finalmente, el Capítulo 7, presenta las conclusiones, y una discusión de trabajo futuro.
Liao, Shu. "Multi-modal image registration using ordinal features and generalized survival exponential entropy /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20LIAO.
Full textKleindeßner, Matthäus [Verfasser], and Ulrike von [Akademischer Betreuer] Luxburg. "Machine learning in a setting of ordinal distance information / Matthäus Kleindeßner ; Betreuer: Ulrike von Luxburg." Tübingen : Universitätsbibliothek Tübingen, 2017. http://d-nb.info/116724415X/34.
Full textPICOZZI, MARTA ANNA ELENA. "Ordinal knowledge and spatial coding of continuous and discrete quantities in infancy." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7794.
Full textTrabelsi, Mariem. "Games with incomplete information : a framework based on possibility theory." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30203.
Full textProbabilistic games with incomplete information, called Bayesian games, offer a suitable framework for games where the utility degrees are additive in essence. This approach does not apply to ordinal games where the utility degrees capture no more than a ranking, nor to situations of decision under qualitative uncertainty. In the first part of this thesis, we propose a representation framework for ordinal games under possibilistic incomplete information (PI-games). These games constitute a suitable framework for the representation of ordinal games under incomplete knowledge. We extend the fundamental notions of secure strategy, pure Nash equilibrium, and mixed Nash equilibrium to this framework. Furthermore, we show that any possibilis- tic game with incomplete information can be transformed into an equivalent normal form game with complete information. The fundamental notions such Nash equilibria (pure and mixed) and secure strategies are in bijection in both frameworks. This representation result is a qualitative counterpart of Harsanyi results about the representation of Bayesian games by normal form games under complete information. It is more of a representation result than the premise of a solving tool. We show that deciding whether a pure Nash equilibrium exists in a PI-game is a difficult task (NP-hard) and propose a Mixed Integer Linear Programming (MILP) encoding of this problem. We also propose a polynomial-time algorithm to find a secure strategy in a PI-game and show that a possibilistic mixed equilibrium can be computed in polynomial time (w.r.t., the size of the game), which contrasts with probabilistic mixed equilibrium computation in cardinal game theory. To confirm the feasibility of the MILP formulation and the polynomial-time algorithms, we introduce a novel generator for PI-games based on the well-known standard normal form game generator: GAMUT. Representing a PI-game in standard normal form requires an extensive expression of the utility functions and the possibility distribution on the product spaces of actions and types. This is the concern of the second part of this thesis where we propose a less costly view of PI-games, namely min-based polymatrix PI-games, which allows to concisely specify PI-games with local interactions, i.e., the interactions between players are pairwise and the utility of a player depends on her neighbors and not on all other players in the PI-game. This framework allows, for instance, the compact representation of coordination games under uncertainty where the satisfaction of a player is high if and only if her strategy is coherent with all of her neighbors, the game being possibly only incompletely known to the players. We show that any 2- player PI-game can be transformed into an equivalent min-based polymatrix game. This result is the qualitative counterpart of Howson and Rosenthal's theorem linking Bayesian games to polymatrix games. Furthermore, as soon as a simple condition on the coherence of the players' knowledge about the world is satisfied, any polymatrix PI-game can be transformed in polynomial time into an equivalent min-based and complete information polymatrix game. We show that the existence of a pure Nash equilibrium in a polymatrix PI-game is an NP-complete problem but no harder than deciding the existence of a pure Nash equilibrium in a PI-game. Finally, we show that the latter family of games can be solved through a MILP formulation. We introduce a novel generator for min-based polymatrix PI-games based on the PI-game generator. Experiments confirm the feasibility of this approach
Hechenbichler, Klaus. "Ensemble-Techniken und ordinale Klassifikation." Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-46296.
Full textJelizarow, Monika. "Global tests of association for multivariate ordinal data." Diss., Ludwig-Maximilians-Universität München, 2015. http://nbn-resolving.de/urn:nbn:de:bvb:19-182787.
Full textBiancheri, Patricia. "Traitement des informations ordinale et phonologique chez l'enfant apprenti lecteur." Lyon 2, 2000. http://theses.univ-lyon2.fr/documents/lyon2/2000/biancheri_p.
Full textBooks on the topic "Ordinal information"
1953-, Albert Jim, ed. Ordinal data modeling. New York: Springer, 1999.
Find full textMoshe, Kress, ed. Ordinal information and preference structures: Decision models and applications. Englewood Cliffs, N.J: Prentice Hall, 1992.
Find full textNational Bureau of Standards. Representation for calendar date and ordinal date for information interchange. Gaithersburg, MD: U.S. Dept. of Commerce/National Bureau of Standards, 1988.
Find full textservice), SpringerLink (Online, ed. Permutation Complexity in Dynamical Systems: Ordinal Patterns, Permutation Entropy and All That. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Find full textWisdom, information, and wonder: What is knowlege for? New York, NY: Routledge, 1991.
Find full textMidgley, Mary. Wisdom, information, and wonder: What is knowledge for? London: Routledge, 1989.
Find full textRonald, Ferns, ed. The learnèd hippopotamus: Poems conveying useful information about animals ordinary and extraordinary. London: Hutchinson, 1986.
Find full textEwart, Gavin. The learnèd hippopotamus: Poems conveying useful information about animals, ordinary and extraordinary. London: Hutchinson, 1986.
Find full textMcGuiggan, Alexander. An analysis of a proposed college based information system specifically focussing on the information needs of the ordinary lecturer. [s.l: The author], 1991.
Find full textKogan, Efim. Ordinary differential equations and calculus of variations. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058922.
Full textBook chapters on the topic "Ordinal information"
Strahringer, Selma, and Rudolf Wille. "Conceptual Clustering via Convex-Ordinal Structures." In Information and Classification, 85–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-50974-2_9.
Full textSrijith, P. K., Shirish Shevade, and S. Sundararajan. "Semi-supervised Gaussian Process Ordinal Regression." In Advanced Information Systems Engineering, 144–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40994-3_10.
Full textJoppen, Tobias, and Johannes Fürnkranz. "Ordinal Monte Carlo Tree Search." In Communications in Computer and Information Science, 39–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89453-5_4.
Full textKatzner, Donald W. "Analysis with Ordinal Measurement." In Unmeasured Information and the Methodology of Social Scientific Inquiry, 197–215. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1629-3_9.
Full textBecerra-Alonso, David, Mariano Carbonero-Ruz, Francisco José Martínez-Estudillo, and Alfonso Carlos Martínez-Estudillo. "Evolutionary Extreme Learning Machine for Ordinal Regression." In Neural Information Processing, 217–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_27.
Full textAzzabi, Arij, Nahla Ben Amor, Hélène Fargier, and Régis Sabbadin. "Ordinal Graph-Based Games." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 271–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50146-4_21.
Full textAntoniuk, Kostiantyn, Vojtěch Franc, and Václav Hlaváč. "MORD: Multi-class Classifier for Ordinal Regression." In Advanced Information Systems Engineering, 96–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40994-3_7.
Full textChen, Ying, Xiao-dong Fu, Kun Yue, Li Liu, and Li-jun Liu. "Ranking Online Services by Aggregating Ordinal Preferences." In Web-Age Information Management, 41–53. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47121-1_4.
Full textSrijith, P. K., Shirish Shevade, and S. Sundararajan. "Validation Based Sparse Gaussian Processes for Ordinal Regression." In Neural Information Processing, 409–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34481-7_50.
Full textWang, Donghui, Junhai Zhai, Hong Zhu, and Xizhao Wang. "An Improved Approach to Ordinal Classification." In Communications in Computer and Information Science, 33–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45652-1_4.
Full textConference papers on the topic "Ordinal information"
Hermiston, Keith. "Aggregating Ordinal Confidences." In 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841360.
Full textBen Amor, Nahla, Hélène Fargier, Régis Sabbadin, and Meriem Trabelsi. "Ordinal Polymatrix Games with Incomplete Information." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/11.
Full textDa San Martino, Giovanni, Wei Gao, and Fabrizio Sebastiani. "Ordinal Text Quantification." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2911451.2914749.
Full textFodor, Janos. "Aggregation of Ordinal Information in Decision Making." In 2007 IEEE International Conference on Computational Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icccyb.2007.4402040.
Full textZhao, Rui, Quan Gan, Shangfei Wang, and Qiang Ji. "Facial Expression Intensity Estimation Using Ordinal Information." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.377.
Full textWang, J., J. Li, and Q. Su. "Monitoring categorical processes by integrating ordinal information." In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2016. http://dx.doi.org/10.1109/ieem.2016.7797869.
Full textWeiqing, Zhuang, and Liu Zhenyu. "Information Measurement Model Based on Ordinal Utility." In 2011 International Conference on Future Computer Sciences and Application (ICFCSA). IEEE, 2011. http://dx.doi.org/10.1109/icfcsa.2011.42.
Full textPérez-Ortiz, M., P. A. Gutiérrez, and C. Hervás-Martínez. "Incorporating Privileged Information to Improve Manifold Ordinal Regression." In International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005075801870194.
Full textXu, Xiao, Qing Zhao, and Ananthram Swami. "Learning Ordinal Information Under Bipartite Stochastic Block Models." In MILCOM 2018 - IEEE Military Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/milcom.2018.8599804.
Full textHuo, Zengwei, and Xin Geng. "Ordinal Zero-Shot Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/266.
Full textReports on the topic "Ordinal information"
Ripey, Mariya. NUMBERS IN THE NEWS TEXT (BASED ON MATERIAL OF ONE ISSUE OF NATIONWIDE NEWSPAPER “DAY”). Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11106.
Full textSoloviev, V. N., and Y. V. Romanenko. Economic analog of Heisenberg uncertainly principle and financial crisis. ESC "IASA" NTUU "Igor Sikorsky Kyiv Polytechnic Institute", May 2017. http://dx.doi.org/10.31812/0564/2463.
Full textVenäläinen, Ari, Sanna Luhtala, Mikko Laapas, Otto Hyvärinen, Hilppa Gregow, Mikko Strahlendorff, Mikko Peltoniemi, et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, January 2021. http://dx.doi.org/10.35614/isbn.9789523361317.
Full textWolf, Shmuel, and William J. Lucas. Involvement of the TMV-MP in the Control of Carbon Metabolism and Partitioning in Transgenic Plants. United States Department of Agriculture, October 1999. http://dx.doi.org/10.32747/1999.7570560.bard.
Full textVargas-Herrera, Hernando, Juan Jose Ospina-Tejeiro, Carlos Alfonso Huertas-Campos, Adolfo León Cobo-Serna, Edgar Caicedo-García, Juan Pablo Cote-Barón, Nicolás Martínez-Cortés, et al. Monetary Policy Report - April de 2021. Banco de la República de Colombia, July 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2021.
Full textFederal Information Processing Standards Publication: representation for calendar date and ordinal date for information interchange. Gaithersburg, MD: National Bureau of Standards, 1988. http://dx.doi.org/10.6028/nbs.fips.4-1.
Full text