Academic literature on the topic 'Retrieval models'
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Journal articles on the topic "Retrieval models"
Lipponen, Antti, Tero Mielonen, Mikko R. A. Pitkänen, Robert C. Levy, Virginia R. Sawyer, Sami Romakkaniemi, Ville Kolehmainen, and Antti Arola. "Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land." Atmospheric Measurement Techniques 11, no. 3 (March 19, 2018): 1529–47. http://dx.doi.org/10.5194/amt-11-1529-2018.
Full textNorquist, Donald C., Paul R. Desrochers, Patrick J. McNicholl, and John R. Roadcap. "A Characterization of Cirrus Cloud Properties That Affect Laser Propagation." Journal of Applied Meteorology and Climatology 47, no. 5 (May 1, 2008): 1322–36. http://dx.doi.org/10.1175/2007jamc1756.1.
Full textKarthikeyan, Lanka, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood. "Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm." Sensors 20, no. 4 (February 24, 2020): 1225. http://dx.doi.org/10.3390/s20041225.
Full textField, R. D., C. Risi, G. A. Schmidt, J. Worden, A. Voulgarakis, A. N. LeGrande, A. H. Sobel, and R. J. Healy. "A Tropospheric Emission Spectrometer HDO/H<sub>2</sub>O retrieval simulator for climate models." Atmospheric Chemistry and Physics Discussions 12, no. 6 (June 5, 2012): 13827–80. http://dx.doi.org/10.5194/acpd-12-13827-2012.
Full textField, R. D., C. Risi, G. A. Schmidt, J. Worden, A. Voulgarakis, A. N. LeGrande, A. H. Sobel, and R. J. Healy. "A Tropospheric Emission Spectrometer HDO/H<sub>2</sub>O retrieval simulator for climate models." Atmospheric Chemistry and Physics 12, no. 21 (November 12, 2012): 10485–504. http://dx.doi.org/10.5194/acp-12-10485-2012.
Full textvan Diedenhoven, B., O. P. Hasekamp, and I. Aben. "Surface pressure retrieval from SCIAMACHY measurements in the O<sub>2</sub>A Band: validation of the measurements and sensitivity on aerosols." Atmospheric Chemistry and Physics Discussions 5, no. 2 (March 14, 2005): 1469–99. http://dx.doi.org/10.5194/acpd-5-1469-2005.
Full textGong, J., and D. L. Wu. "CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances." Atmospheric Measurement Techniques 7, no. 6 (June 26, 2014): 1873–90. http://dx.doi.org/10.5194/amt-7-1873-2014.
Full textGong, J., and D. L. Wu. "CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances." Atmospheric Measurement Techniques Discussions 6, no. 5 (September 4, 2013): 8187–233. http://dx.doi.org/10.5194/amtd-6-8187-2013.
Full textChimah, Jonathan N., and Friday Ibiam Ude. "Current trends in information retrieval systems: review of fuzzy set theory and fuzzy Boolean retrieval models." Journal of Library Services and Technologies 2, no. 2 (June 2020): 48–56. http://dx.doi.org/10.47524/jlst.v2i2.5.
Full textvan Diedenhoven, B., O. P. Hasekamp, and I. Aben. "Surface pressure retrieval from SCIAMACHY measurements in the O<sub>2</sub> A Band: validation of the measurements and sensitivity on aerosols." Atmospheric Chemistry and Physics 5, no. 8 (August 11, 2005): 2109–20. http://dx.doi.org/10.5194/acp-5-2109-2005.
Full textDissertations / Theses on the topic "Retrieval models"
Stathopoulos, Vassilios. "Generative probabilistic models for image retrieval." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3360/.
Full textMorgan, Richard. "Component library retrieval using property models." Thesis, Durham University, 1991. http://etheses.dur.ac.uk/6095/.
Full textVasconcelos, Nuno Miguel Borges de Pinho Cruz de. "Bayesian models for visual information retrieval." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/62947.
Full textIncludes bibliographical references (leaves 192-208).
This thesis presents a unified solution to visual recognition and learning in the context of visual information retrieval. Realizing that the design of an effective recognition architecture requires careful consideration of the interplay between feature selection, feature representation, and similarity function, we start by searching for a performance criteria that can simultaneously guide the design of all three components. A natural solution is to formulate visual recognition as a decision theoretical problem, where the goal is to minimize the probability of retrieval error. This leads to a Bayesian architecture that is shown to generalize a significant number of previous recognition approaches, solving some of the most challenging problems faced by these: joint modeling of color and texture, objective guidelines for controlling the trade-off between feature transformation and feature representation, and unified support for local and global queries without requiring image segmentation. The new architecture is shown to perform well on color, texture, and generic image databases, providing a good trade-off between retrieval accuracy, invariance, perceptual relevance of similarity judgments, and complexity. Because all that is needed to perform optimal Bayesian decisions is the ability to evaluate beliefs on the different hypothesis under consideration, a Bayesian architecture is not restricted to visual recognition. On the contrary, it establishes a universal recognition language (the language of probabilities) that provides a computational basis for the integration of information from multiple content sources and modalities. In result, it becomes possible to build retrieval systems that can simultaneously account for text, audio, video, or any other content modalities. Since the ability to learn follows from the ability to integrate information over time, this language is also conducive to the design of learning algorithms. We show that learning is, indeed, an important asset for visual information retrieval by designing both short and long-term learning mechanisms. Over short time scales (within a retrieval session), learning is shown to assure faster convergence to the desired target images. Over long time scales (between retrieval sessions), it allows the retrieval system to tailor itself to the preferences of particular users. In both cases, all the necessary computations are carried out through Bayesian belief propagation algorithms that, although optimal in a decision-theoretic sense, are extremely simple, intuitive, and easy to implement.
by Nuno Miguel Borges de Pinho Cruz de Vasconcelos.
Ph.D.
Mensink, Thomas. "Learning Image Classification and Retrieval Models." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM113/document.
Full textWe are currently experiencing an exceptional growth of visual data, for example, millions of photos are shared daily on social-networks. Image understanding methods aim to facilitate access to this visual data in a semantically meaningful manner. In this dissertation, we define several detailed goals which are of interest for the image understanding tasks of image classification and retrieval, which we address in three main chapters. First, we aim to exploit the multi-modal nature of many databases, wherein documents consists of images with a form of textual description. In order to do so we define similarities between the visual content of one document and the textual description of another document. These similarities are computed in two steps, first we find the visually similar neighbors in the multi-modal database, and then use the textual descriptions of these neighbors to define a similarity to the textual description of any document. Second, we introduce a series of structured image classification models, which explicitly encode pairwise label interactions. These models are more expressive than independent label predictors, and lead to more accurate predictions. Especially in an interactive prediction scenario where a user provides the value of some of the image labels. Such an interactive scenario offers an interesting trade-off between accuracy and manual labeling effort. We explore structured models for multi-label image classification, for attribute-based image classification, and for optimizing for specific ranking measures. Finally, we explore k-nearest neighbors and nearest-class mean classifiers for large-scale image classification. We propose efficient metric learning methods to improve classification performance, and use these methods to learn on a data set of more than one million training images from one thousand classes. Since both classification methods allow for the incorporation of classes not seen during training at near-zero cost, we study their generalization performances. We show that the nearest-class mean classification method can generalize from one thousand to ten thousand classes at negligible cost, and still perform competitively with the state-of-the-art
Rebai, Ahmed. "Interactive Object Retrieval using Interpretable Visual Models." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00608467.
Full textPérez-Sancho, Carlos. "Stochastic language models for music information retrieval." Doctoral thesis, Universidad de Alicante, 2009. http://hdl.handle.net/10045/14217.
Full textTam, Kwok Leung. "Indexing and retrieval of 3D articulated geometry models." Thesis, Durham University, 2009. http://etheses.dur.ac.uk/21/.
Full textMjali, Siyabonga Zimozoxolo. "Latent semantic models : a study of probabilistic models for text in information retrieval." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/73881.
Full textMini Dissertation (MSc)--University of Pretoria, 2020.
The Hub Internship
Centre for Artificial Intelligence Research
Statistics
MSc (Mathematical Statistics)
Unrestricted
Belkacem, Thiziri. "Neural models for information retrieval : towards asymmetry sensitive approaches based on attention models." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30167.
Full textThis work is situated in the context of information retrieval (IR) using machine learning (ML) and deep learning (DL) techniques. It concerns different tasks requiring text matching, such as ad-hoc research, question answering and paraphrase identification. The objective of this thesis is to propose new approaches, using DL methods, to construct semantic-based models for text matching, and to overcome the problems of vocabulary mismatch related to the classical bag of word (BoW) representations used in traditional IR models. Indeed, traditional text matching methods are based on the BoW representation, which considers a given text as a set of independent words. The process of matching two sequences of text is based on the exact matching between words. The main limitation of this approach is related to the vocabulary mismatch. This problem occurs when the text sequences to be matched do not use the same vocabulary, even if their subjects are related. For example, the query may contain several words that are not necessarily used in the documents of the collection, including relevant documents. BoW representations ignore several aspects about a text sequence, such as the structure the context of words. These characteristics are important and make it possible to differentiate between two texts that use the same words but expressing different information. Another problem in text matching is related to the length of documents. The relevant parts can be distributed in different ways in the documents of a collection. This is especially true in large documents that tend to cover a large number of topics and include variable vocabulary. A long document could thus contain several relevant passages that a matching model must capture. Unlike long documents, short documents are likely to be relevant to a specific subject and tend to contain a more restricted vocabulary. Assessing their relevance is in principle simpler than assessing the one of longer documents. In this thesis, we have proposed different contributions, each addressing one of the above-mentioned issues. First, in order to solve the problem of vocabulary mismatch, we used distributed representations of words (word embedding) to allow a semantic matching between the different words. These representations have been used in IR applications where document/query similarity is computed by comparing all the term vectors of the query with all the term vectors of the document, regardless. Unlike the models proposed in the state-of-the-art, we studied the impact of query terms regarding their presence/absence in a document. We have adopted different document/query matching strategies. The intuition is that the absence of the query terms in the relevant documents is in itself a useful aspect to be taken into account in the matching process. Indeed, these terms do not appear in documents of the collection for two possible reasons: either their synonyms have been used or they are not part of the context of the considered documents. The methods we have proposed make it possible, on the one hand, to perform an inaccurate matching between the document and the query, and on the other hand, to evaluate the impact of the different terms of a query in the matching process. Although the use of word embedding allows semantic-based matching between different text sequences, these representations combined with classical matching models still consider the text as a list of independent elements (bag of vectors instead of bag of words). However, the structure of the text as well as the order of the words is important. Any change in the structure of the text and/or the order of words alters the information expressed. In order to solve this problem, neural models were used in text matching
Wessel, Raoul [Verfasser]. "Shape Retrieval Methods for Architectural 3D Models / Raoul Wessel." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1048091503/34.
Full textBooks on the topic "Retrieval models"
Losee, Robert M. Text retrieval and filtering: Analytic models of performance. Dordrecht: Springer, 1998.
Find full textText retrieval and filtering: Analytic models of performance. Boston: Kluwer Academic Publishers, 1998.
Find full textCrestani, Fabio. Information Retrieval: Uncertainty and Logics: Advanced Models for the Representation and Retrieval of Information. Boston, MA: Springer US, 1998.
Find full textAerts, Diederik, Andrei Khrennikov, Massimo Melucci, and Bourama Toni, eds. Quantum-Like Models for Information Retrieval and Decision-Making. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25913-6.
Full textBenois-Pineau, Jenny, and Patrick Le Callet, eds. Visual Content Indexing and Retrieval with Psycho-Visual Models. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57687-9.
Full textNext generation search engines: Advanced models for information retrieval. Hershey PA: Information Science Reference, 2012.
Find full textRambaud, Salvador Cruz. Algebraic models for accounting systems. Singapore: World Scientific, 2010.
Find full textQiu, Liwen. Probabilistic models of search state and path patterns in hypertext information retrieval systems. Ottawa: National Library of Canada, 1991.
Find full textPaquette, Gilbert. Visual knowledge modeling for semantic web technologies: Models and ontologies. Hershey, PA: Information Science Reference, 2010.
Find full textLester, Neil. Assisting the software reuse process through classification and retrieval of software models. [s.l: The Author], 2000.
Find full textBook chapters on the topic "Retrieval models"
Stein, Benno, Tim Gollub, and Maik Anderka. "Retrieval Models." In Encyclopedia of Social Network Analysis and Mining, 1583–86. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_117.
Full textStein, Benno, Tim Gollub, and Maik Anderka. "Retrieval Models." In Encyclopedia of Social Network Analysis and Mining, 2251–56. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_117.
Full textStein, Benno, Tim Gollub, and Maik Anderka. "Retrieval Models." In Encyclopedia of Social Network Analysis and Mining, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_117-1.
Full textWesterveld, Thijs, Arjen de Vries, and Franciska de Jong. "Generative Probabilistic Models." In Multimedia Retrieval, 177–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_6.
Full textCeri, Stefano, Alessandro Bozzon, Marco Brambilla, Emanuele Della Valle, Piero Fraternali, and Silvia Quarteroni. "Information Retrieval Models." In Web Information Retrieval, 27–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39314-3_3.
Full textAmati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems, 1976–81. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_916.
Full textAmati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems, 1523–28. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_916.
Full textAmati, Giambattista. "Information Retrieval Models." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_916-2.
Full textMetzler, Donald. "Classical Retrieval Models." In A Feature-Centric View of Information Retrieval, 7–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22898-8_2.
Full textDominich, Sándor. "Information Retrieval Models." In Mathematical Modelling: Theory and Applications, 95–159. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0752-8_3.
Full textConference papers on the topic "Retrieval models"
Li, Min, J. Y. H. Fuh, Y. F. Zhang, and Z. M. Qiu. "General and Partial Shape Matching Approaches on Feature-Based CAD Models to Support Efficient Part Retrieval." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49159.
Full textHorstmann, Jochen, Wolfgang Koch, and Susanne Lehner. "High Resolution Wind Fields Retrieved From Spaceborne Synthetic Aperture Radar Images in Comparison to Numerical Models." In ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/omae2002-28301.
Full textde Rijke, Maarten. "Session details: Retrieval Models." 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/10.1145/3252665.
Full textUjjwal, Dasu, Prakhar Rastogi, and Siril Siddhartha. "Analysis of retrieval models for cross language information retrieval." In 2016 10th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2016. http://dx.doi.org/10.1109/isco.2016.7727028.
Full textUdoyen, Nsikan, and David W. Rosen. "Description Logic Representation of Finite Element Analysis Models for Automated Retrieval." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99451.
Full textNallapati, Ramesh. "Discriminative models for information retrieval." In the 27th annual international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1008992.1009006.
Full textChoi, Jaeho, W. Bruce Croft, and Jin Young Kim. "Quality models for microblog retrieval." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398527.
Full textLipka, Nedim, and Benno Stein. "Robust Models in Information Retrieval." In 2011 22nd International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2011. http://dx.doi.org/10.1109/dexa.2011.73.
Full textBollmann, P., and S. K. M. Wong. "Adaptive linear information retrieval models." In the 10th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/42005.42023.
Full textCortelazzo, G. M., and N. Orio. "Retrieval of Colored 3D Models." In Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06). IEEE, 2006. http://dx.doi.org/10.1109/3dpvt.2006.121.
Full textReports on the topic "Retrieval models"
Conser, Erik. Improved Scoring Models for Semantic Image Retrieval Using Scene Graphs. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5767.
Full textMcFarquhar, Greg. Determining Best Estimates and Uncertainties in Cloud Microphysical Parameters from ARM Field Data: Implications for Models, Retrieval Schemes and Aerosol-Cloud-Radiation Interactions. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1233352.
Full textNg, Kenney. A Maximum Likelihood Ratio Information Retrieval Model. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456243.
Full textFordham, R. A. Tank waste remediation system simulation analysis retrieval model. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/327599.
Full textForbus, Kenneth D., Dedre Gentner, and Keith Law. MAC/FAC: A Model of Similarity-Based Retrieval. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada286291.
Full textForbus, Kenneth D., Dedre Gentner, and Keith Law. MAC/FAC: A Model of Similarity-Based Retrieval. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada288515.
Full textClark, H. The Opstat Client-Server Model for Statistics Retrieval. RFC Editor, September 1995. http://dx.doi.org/10.17487/rfc1856.
Full textSchillings, P. L., D. L. Heiser, and S. P. Fogdall. Buried waste program retrieval process conceptual model design. Office of Scientific and Technical Information (OSTI), December 1988. http://dx.doi.org/10.2172/6329901.
Full textHeiser, D. L., and S. P. Fogdall. Buried waste program retrieval process model: Revision 1. Office of Scientific and Technical Information (OSTI), March 1989. http://dx.doi.org/10.2172/6348672.
Full textVachon, P. W., and F. W. Dobson. Wind Retrieval from RADARSAT SAR Images: Selection of a Suitable C-band HH Polarization Wind Retrieval Model. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219536.
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