Academic literature on the topic 'Understanding of data models'
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Journal articles on the topic "Understanding of data models"
Shanks, Graeme, and Peta Darke. "Understanding corporate data models." Information & Management 35, no. 1 (January 1999): 19–30. http://dx.doi.org/10.1016/s0378-7206(98)00078-0.
Full textFrench, Robert M., and Maud Jacquet. "Understanding bilingual memory: models and data." Trends in Cognitive Sciences 8, no. 2 (February 2004): 87–93. http://dx.doi.org/10.1016/j.tics.2003.12.011.
Full textDeBruine, Lisa M., and Dale J. Barr. "Understanding Mixed-Effects Models Through Data Simulation." Advances in Methods and Practices in Psychological Science 4, no. 1 (January 2021): 251524592096511. http://dx.doi.org/10.1177/2515245920965119.
Full textKnüsel, Benedikt, and Christoph Baumberger. "Understanding climate phenomena with data-driven models." Studies in History and Philosophy of Science Part A 84 (December 2020): 46–56. http://dx.doi.org/10.1016/j.shpsa.2020.08.003.
Full textDurant, Szonya. "Zhaoping, L. Understanding Vision: Theory, Models, and Data." Perception 45, no. 10 (July 19, 2016): 1207–8. http://dx.doi.org/10.1177/0301006616660638.
Full textBest, Nicky, and Peter Green. "Structure and uncertainty: Graphical models for understanding complex data." Significance 2, no. 4 (November 30, 2005): 177–81. http://dx.doi.org/10.1111/j.1740-9713.2005.00133.x.
Full textSteinberg, David M., and Dizza Bursztyn. "Data Analytic Tools for Understanding Random Field Regression Models." Technometrics 46, no. 4 (November 2004): 411–20. http://dx.doi.org/10.1198/004017004000000419.
Full textCagetti, Marco, and Mariacristina De Nardi. "WEALTH INEQUALITY: DATA AND MODELS." Macroeconomic Dynamics 12, S2 (September 2008): 285–313. http://dx.doi.org/10.1017/s1365100507070150.
Full textButts, Daniel A. "Data-Driven Approaches to Understanding Visual Neuron Activity." Annual Review of Vision Science 5, no. 1 (September 15, 2019): 451–77. http://dx.doi.org/10.1146/annurev-vision-091718-014731.
Full textYoo, Kang Min, Youhyun Shin, and Sang-goo Lee. "Data Augmentation for Spoken Language Understanding via Joint Variational Generation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7402–9. http://dx.doi.org/10.1609/aaai.v33i01.33017402.
Full textDissertations / Theses on the topic "Understanding of data models"
Sommeria-Klein, Guilhem. "From models to data : understanding biodiversity patterns from environmental DNA data." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30390/document.
Full textIntegrative patterns of biodiversity, such as the distribution of taxa abundances and the spatial turnover of taxonomic composition, have been under scrutiny from ecologists for a long time, as they offer insight into the general rules governing the assembly of organisms into ecological communities. Thank to recent progress in high-throughput DNA sequencing, these patterns can now be measured in a fast and standardized fashion through the sequencing of DNA sampled from the environment (e.g. soil or water), instead of relying on tedious fieldwork and rare naturalist expertise. They can also be measured for the whole tree of life, including the vast and previously unexplored diversity of microorganisms. Taking full advantage of this new type of data is challenging however: DNA-based surveys are indirect, and suffer as such from many potential biases; they also produce large and complex datasets compared to classical censuses. The first goal of this thesis is to investigate how statistical tools and models classically used in ecology or coming from other fields can be adapted to DNA-based data so as to better understand the assembly of ecological communities. The second goal is to apply these approaches to soil DNA data from the Amazonian forest, the Earth's most diverse land ecosystem. Two broad types of mechanisms are classically invoked to explain the assembly of ecological communities: 'neutral' processes, i.e. the random birth, death and dispersal of organisms, and 'niche' processes, i.e. the interaction of the organisms with their environment and with each other according to their phenotype. Disentangling the relative importance of these two types of mechanisms in shaping taxonomic composition is a key ecological question, with many implications from estimating global diversity to conservation issues. In the first chapter, this question is addressed across the tree of life by applying the classical analytic tools of community ecology to soil DNA samples collected from various forest plots in French Guiana. The second chapter focuses on the neutral aspect of community assembly.[...]
Kivinen, Jyri Juhani. "Statistical models for natural scene data." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/8879.
Full textSteinberg, Daniel. "An Unsupervised Approach to Modelling Visual Data." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9415.
Full textDas, Debasish. "Bayesian Sparse Regression with Application to Data-driven Understanding of Climate." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/313587.
Full textPh.D.
Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommodate extremes and other domain constraints such as dynamical relations among variables, spatial and temporal constraints, need to provide uncertainty estimates and feature correlations, among others. We adopted a hierarchical Bayesian version of the sparse regression framework and exploited its inherent flexibility to accommodate the constraints. We applied sparse regression for the feature selection problem of statistical downscaling of the climate variables with particular focus on their extremes. This is important for many impact studies where the climate change information is required at a spatial scale much finer than that provided by the global or regional climate models. Characterizing the dependence of extremes on covariates can help in identification of plausible causal drivers and inform extremes downscaling. We propose a general-purpose sparse Bayesian framework for covariate discovery that accommodates the non-Gaussian distribution of extremes within a hierarchical Bayesian sparse regression model. We obtain posteriors over regression coefficients, which indicate dependence of extremes on the corresponding covariates and provide uncertainty estimates, using a variational Bayes approximation. The method is applied for selecting informative atmospheric covariates at multiple spatial scales as well as indices of large scale circulation and global warming related to frequency of precipitation extremes over continental United States. Our results confirm the dependence relations that may be expected from known precipitation physics and generates novel insights which can inform physical understanding. We plan to extend our model to discover covariates for extreme intensity in future. We further extend our framework to handle the dynamic relationship among the climate variables using a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP). The extended model can achieve simultaneous clustering and discovery of covariates within each cluster. Moreover, the a priori knowledge about association between pairs of data-points is incorporated in the model through must-link constraints on a Markov Random Field (MRF) prior. A scalable and efficient variational Bayes approach is developed to infer posteriors on regression coefficients and cluster variables.
Temple University--Theses
LaMar, Michelle Marie. "Models for understanding student thinking using data from complex computerized science tasks." Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3686374.
Full textThe Next Generation Science Standards (NGSS Lead States, 2013) define performance targets which will require assessment tasks that can integrate discipline knowledge and cross-cutting ideas with the practices of science. Complex computerized tasks will likely play a large role in assessing these standards, but many questions remain about how best to make use of such tasks within a psychometric framework (National Research Council, 2014). This dissertation explores the use of a more extensive cognitive modeling approach, driven by the extra information contained in action data collected while students interact with complex computerized tasks. Three separate papers are included. In Chapter 2, a mixture IRT model is presented that simultaneously classifies student understanding of a task while measuring student ability within their class. The model is based on differentially scoring the subtask action data from a complex performance. Simulation studies show that both class membership and class-specific ability can be reasonably estimated given sufficient numbers of items and response alternatives. The model is then applied to empirical data from a food-web task, providing some evidence of feasibility and validity. Chapter 3 explores the potential of using a more complex cognitive model for assessment purposes. Borrowing from the cognitive science domain, student decisions within a strategic task are modeled with a Markov decision process. Psychometric properties of the model are explored and simulation studies report on parameter recovery within the context of a simple strategy game. In Chapter 4 the Markov decision process (MDP) measurement model is then applied to an educational game to explore the practical benefits and difficulties of using such a model with real world data. Estimates from the MDP model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.
Maloo, Akshay. "Dynamic Behavior Visualizer: A Dynamic Visual Analytics Framework for Understanding Complex Networked Models." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25296.
Full textMaster of Science
Izumi, Kenji. "Application of Paleoenvironmental Data for Testing Climate Models and Understanding Past and Future Climate Variations." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18510.
Full textLipecki, Johan, and Viggo Lundén. "The Effect of Data Quantity on Dialog System Input Classification Models." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282.
Full textDetta arbete undersöker hur olika datamängder påverkar olika slags ordvektormodeller för klassificering av indata till dialogsystem. Hypotesen att det finns ett tröskelvärde för träningsdatamängden där täta ordvektormodeller när den högsta moderna utvecklingsnivån samt att n-gram-ordvektor-klassificerare med bokstavs-noggrannhet lämpar sig särskilt väl för svenska klassificerare söks bevisas med stöd i att sammansättningar är särskilt produktiva i svenskan och att bokstavs-noggrannhet i modellerna gör att tidigare osedda ord kan klassificeras. Dessutom utvärderas hypotesen att klassificerare som tränas med enkla påståenden är bättre lämpade att klassificera indata i chattkonversationer än klassificerare som tränats med hela chattkonversationer. Resultaten stödjer ingendera hypotes utan visar istället att glesa vektormodeller presterar väldigt väl i de genomförda klassificeringstesterna. Utöver detta visar resultaten att datamängden 799 544 ord inte räcker till för att träna täta ordvektormodeller väl men att konversationer räcker gott och väl för att träna modeller för klassificering av frågor och påståenden i chattkonversationer, detta eftersom de modeller som tränats med användarindata, påstående för påstående, snarare än hela chattkonversationer, inte resulterar i bättre klassificerare för chattpåståenden.
Abufouda, Mohammed [Verfasser], and Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig." Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.
Full textWojatzki, Michael Maximilian [Verfasser], and Torsten [Akademischer Betreuer] Zesch. "Computer-assisted understanding of stance in social media : formalizations, data creation, and prediction models / Michael Maximilian Wojatzki ; Betreuer: Torsten Zesch." Duisburg, 2019. http://d-nb.info/1177681471/34.
Full textBooks on the topic "Understanding of data models"
Ashwin, Ram, and Moorman Kenneth, eds. Understanding language understanding: Computational models of reading. Cambridge, Mass: MIT Press, 1999.
Find full textKlüver, Jürgen. Social Understanding: On Hermeneutics, Geometrical Models and Artificial Intelligence. Dordrecht: Springer Science+Business Media B.V., 2011.
Find full textBakeman, Roger. Understanding log-linear analysis with ILOG: An interactive approach. Hillsdale, N.J: L. Erlbaum, 1994.
Find full textBerry, Joseph K. Map analysis: Understanding spatial patterns and relationships. San Francisco, CA: GeoTec Media, 2007.
Find full textMeju, Max A. Geophysical data analysis: Understanding inverse problem theory and practice. Tulsa, OK: Society of Exploration Geophysicists, 1994.
Find full textAlvarado, Sergio Jose. Understanding editorial text: A computer model of argument comprehension. Boston: Kluwer Academic Publishers, 1990.
Find full text1941-, Taylor Arlene G., ed. Understanding FRBR: What it is and how it will affect our retrieval tools. Westport, Conn: Libraries Unlimited, 2007.
Find full textErickson, Bonnie H. Understanding data. 2nd ed. Toronto: University of Toronto Press, 1992.
Find full textErickson, Bonnie H. Understanding data. 2nd ed. Buckingham: Open University Press, 1992.
Find full textKauffels, Franz-Joachim. Understanding data communications. Chichester, West Sussex, England: Ellis Horwood, 1989.
Find full textBook chapters on the topic "Understanding of data models"
Westfall, Peter H., and Andrea L. Arias. "Censored Data Models." In Understanding Regression Analysis, 379–403. Boca Raton : CRC Press, [2020]: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764-15.
Full textDavid, Salsburg. "5 Models Versus Data." In Understanding Randomness, 85–96. CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742: CRC Press, 2017. http://dx.doi.org/10.1201/9780203734674-7.
Full textHolloway, Paul. "Spatial Data Models." In Understanding GIS through Sustainable Development Goals, 21–42. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003220510-5.
Full textMatthews, David Edward, and Vernon Todd Farewell. "12 Regression Models for Count Data." In Using and Understanding Medical Statistics, 141–48. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099427.
Full textBadiru, Adedeji B. "Data Analytics Tools for Understanding Random Field Regression Models *." In Data Analytics, 211–32. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis: CRC Press, 2020. http://dx.doi.org/10.1201/9781003083146-7.
Full textHand, David J. "Intelligent Data Analysis and Deep Understanding." In Causal Models and Intelligent Data Management, 67–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_5.
Full textJung, Dominik. "Business Data Understanding." In The Modern Business Data Analyst, 49–110. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59907-1_3.
Full textMatthews, David Edward, and Vernon Todd Farewell. "10 Linear Regression Models for Medical Data." In Using and Understanding Medical Statistics, 111–27. Basel: KARGER, 2007. http://dx.doi.org/10.1159/000099425.
Full textLiiv, Innar. "Understanding the Data Model." In Behaviormetrics: Quantitative Approaches to Human Behavior, 1–13. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2418-6_1.
Full textWalloth, Christian, Ernst Gebetsroither-Geringer, and Funda Atun. "Introduction: Overcoming Limitations of Urban Systems Models and of Data Availability." In Understanding Complex Systems, 1–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30178-5_1.
Full textConference papers on the topic "Understanding of data models"
Honavar, Vasant. "Learning predictive models from large distributed autonomous data sources." In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382178.
Full textHolland, Marika. "Investigation of the climate system using Earth System models." In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382181.
Full textGorinevsky, Dimitry, Bryan Matthews, and Rodney Martin. "Aircraft anomaly detection using performance models trained on fleet data." In 2012 Conference on Intelligent Data Understanding (CIDU). IEEE, 2012. http://dx.doi.org/10.1109/cidu.2012.6382196.
Full textSatkin, Scott, Jason Lin, and Martial Hebert. "Data-Driven Scene Understanding from 3D Models." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.128.
Full textCui, Jia, Yonggang Deng, and Bowen Zhou. "Reinforcing language model for speech translation with auxiliary data." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373308.
Full textPek, Yun Ning, and Kwan Hui Lim. "Identifying and Understanding Business Trends using Topic Models with Word Embedding." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005497.
Full textKatsumaru, Masaki, Mikio Nakano, Kazunori Komatani, Kotaro Funakoshi, Tetsuya Ogata, and Hiroshi G. Okuno. "Improving speech understanding accuracy with limited training data using multiple language models and multiple understanding models." In Interspeech 2009. ISCA: ISCA, 2009. http://dx.doi.org/10.21437/interspeech.2009-699.
Full textShih-Hung Liu, Fang-Hui Chu, Shih-Hsiang Lin, Hung-Shin Lee, and Berlin Chen. "Training data selection for improving discriminative training of acoustic models." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430125.
Full textHoernle, Nicholas, Kobi Gal, Barbara Grosz, Leilah Lyons, Ada Ren, and Andee Rubin. "Interpretable Models for Understanding Immersive Simulations." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/321.
Full textJin, Ruoming, Dong Li, Jing Gao, Zhi Liu, Li Chen, and Yang Zhou. "Towards a Better Understanding of Linear Models for Recommendation." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467428.
Full textReports on the topic "Understanding of data models"
Glass, Samuel V., Samuel L. Zelinka, Charles R. Boardman, and Emil Engelund Thybring. Promoting advances in understanding water vapor sorption in wood: relegating popular models and misconceptions. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541615744.
Full textWeijters, Bert. Analyzing Experimental Data in Structural Equation Models. Instats Inc., 2023. http://dx.doi.org/10.61700/zclk0a8vgkfaa706.
Full textGruber, Peter. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/pmgm4wmm7ffer469.
Full textGruber, Peter H. Using ChatGPT for Advanced Data Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/zqir2dzchct5b469.
Full textGruber, Peter. Using ChatGPT for Advanced Data Analysis 2.0. Instats Inc., 2023. http://dx.doi.org/10.61700/txvjolg6id2hj469.
Full textGrimm, Kevin. Factor Analysis and Measurement Invariance with Categorical Data in Mplus. Instats Inc., 2024. http://dx.doi.org/10.61700/2c14h0c6ktix9661.
Full textGrimm, Kevin. Factor Analysis and Measurement Invariance with Categorical Data in R. Instats Inc., 2024. http://dx.doi.org/10.61700/6q6pcruvzduci667.
Full textde Padua, David, Matteo Lanzafame, Irfan Qureshi, and Kiyoshi Taniguchi. Understanding the Drivers of Remittances to Pakistan. Asian Development Bank, July 2024. http://dx.doi.org/10.22617/wps240348-2.
Full textAltman, Safra, Krystyna Powell, and Marin Kress. Marine bioinvasion risk : review of current ecological models. Engineer Research and Development Center (U.S.), October 2023. http://dx.doi.org/10.21079/11681/47820.
Full textLieng, Sotberg, and Brennodden. L51570 Energy Based Pipe-Soil Interaction Models. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 1988. http://dx.doi.org/10.55274/r0010091.
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