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Artykuły w czasopismach na temat "Latent"
Keeling, R. "Latest developments in latent defects insurance". Property Management 11, nr 3 (marzec 1993): 220–25. http://dx.doi.org/10.1108/eum0000000003400.
Pełny tekst źródłaWolfson, Susan J. "Yeats's Latent Keats / Keats's Latent Yeats". PMLA/Publications of the Modern Language Association of America 131, nr 3 (maj 2016): 603–21. http://dx.doi.org/10.1632/pmla.2016.131.3.603.
Pełny tekst źródłaMcCutcheon, Allen L., Rolf Langeheine i Jurgen Rost. "Latent Trait and Latent Class Models." Contemporary Sociology 18, nr 5 (wrzesień 1989): 836. http://dx.doi.org/10.2307/2073408.
Pełny tekst źródłaGuo, J. "Latent class regression on latent factors". Biostatistics 7, nr 1 (25.05.2005): 145–63. http://dx.doi.org/10.1093/biostatistics/kxi046.
Pełny tekst źródłaFLORES, RICARDO, SONIA DELGADO, MARÍA-ELENA RODIO, SILVIA AMBRÓS, CARMEN HERNÁNDEZ i FRANCESCO DI SERIO. "Peach latent mosaic viroid: not so latent". Molecular Plant Pathology 7, nr 4 (lipiec 2006): 209–21. http://dx.doi.org/10.1111/j.1364-3703.2006.00332.x.
Pełny tekst źródłaCroon, Marcel. "Latent class analysis with ordered latent classe". British Journal of Mathematical and Statistical Psychology 43, nr 2 (listopad 1990): 171–92. http://dx.doi.org/10.1111/j.2044-8317.1990.tb00934.x.
Pełny tekst źródłaChaudhary, Neha, i Priti Dimri. "LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK". Indian Journal of Computer Science and Engineering 12, nr 5 (20.10.2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.
Pełny tekst źródłaZellweger, Jean-Pierre. "Latent Tuberculosis Infection". European Respiratory & Pulmonary Diseases 4, nr 1 (2018): 21. http://dx.doi.org/10.17925/erpd.2018.4.1.21.
Pełny tekst źródłaIQBAL, JAVED, ALTAF HUSSAIN MALIK i Aftab JAMIL. "LATENT TUBERCULOSIS;". Professional Medical Journal 19, nr 01 (3.01.2012): 059–62. http://dx.doi.org/10.29309/tpmj/2012.19.01.1949.
Pełny tekst źródłaToister, Yanai. "Latent digital". Journal of Visual Art Practice 19, nr 2 (14.01.2020): 125–36. http://dx.doi.org/10.1080/14702029.2019.1701915.
Pełny tekst źródłaRozprawy doktorskie na temat "Latent"
Anaya, Leticia H. "Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers". Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103284/.
Pełny tekst źródłaXiong, Hao. "Diversified Latent Variable Models". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18512.
Pełny tekst źródłaEtessami, Pantea. "Mutagenesis studies on the genome of cassava latent virus : (African cassava latent virus)". Thesis, University of East Anglia, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235620.
Pełny tekst źródłaMurphy, Sean Michael. "Disease management and latent choices". Online access for everyone, 2008. http://www.dissertations.wsu.edu/Dissertations/Summer2008/S_Murphy_062608.pdf.
Pełny tekst źródłaCartmill, Ian. "Builders' liability for latent defects". Thesis, University of Oxford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302694.
Pełny tekst źródłaShafia, Aminath. "Latent infection of Botrytis cinerea". Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499372.
Pełny tekst źródłaPonweiser, Martin. "Latent Dirichlet Allocation in R". WU Vienna University of Economics and Business, 2012. http://epub.wu.ac.at/3558/1/main.pdf.
Pełny tekst źródłaSeries: Theses / Institute for Statistics and Mathematics
Creagh-Osborne, Jane. "Latent variable generalized linear models". Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1885.
Pełny tekst źródłaMao, Cheng Ph D. Massachusetts Institute of Technology. "Matrix estimation with latent permutations". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117863.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 151-167).
Motivated by various applications such as seriation, network alignment and ranking from pairwise comparisons, we study the problem of estimating a structured matrix with rows and columns shuffled by latent permutations, given noisy and incomplete observations of its entries. This problem is at the intersection of shape constrained estimation which has a long history in statistics, and latent permutation learning which has driven a recent surge of interest in the machine learning community. Shape constraints on matrices, such as monotonicity and smoothness, are generally more robust than parametric assumptions, and often allow for adaptive and efficient estimation in high dimensions. On the other hand, latent permutations underlie many graph matching and assignment problems that are computationally intractable in the worst-case and not yet well-understood in the average-case. Therefore, it is of significant interest to both develop statistical approaches and design efficient algorithms for problems where shape constraints meet latent permutations. In this work, we consider three specific models: the statistical seriation model, the noisy sorting model and the strong stochastic transitivity model. First, statistical seriation consists in permuting the rows of a noisy matrix in such a way that all its columns are approximately monotone, or more generally, unimodal. We study both global and adaptive rates of estimation for this model, and introduce an efficient algorithm for the monotone case. Next, we move on to ranking from pairwise comparisons, and consider the noisy sorting model. We establish the minimax rates of estimation for noisy sorting, and propose a near-linear time multistage algorithm that achieves a near-optimal rate. Finally, we study the strong stochastic transitivity model that significantly generalizes the noisy sorting model for estimation from pairwise comparisons. Our efficient algorithm achieves the rate (n- 3 /4 ), narrowing a gap between the statistically optimal rate Õ(n-1 ) and the state-of-the-art computationally efficient rate [Theta] (n- 1/ 2 ). In addition, we consider the scenario where a fixed subset of pairwise comparisons is given. A dichotomy exists between the worst-case design, where consistent estimation is often impossible, and an average-case design, where we show that the optimal rate of estimation depends on the degree sequence of the comparison topology.
by Cheng Mao.
Ph. D.
Dallaire, Patrick. "Bayesian nonparametric latent variable models". Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26848.
Pełny tekst źródłaOne of the important problems in machine learning is determining the complexity of the model to learn. Too much complexity leads to overfitting, which finds structures that do not actually exist in the data, while too low complexity leads to underfitting, which means that the expressiveness of the model is insufficient to capture all the structures present in the data. For some probabilistic models, the complexity depends on the introduction of one or more latent variables whose role is to explain the generative process of the data. There are various approaches to identify the appropriate number of latent variables of a model. This thesis covers various Bayesian nonparametric methods capable of determining the number of latent variables to be used and their dimensionality. The popularization of Bayesian nonparametric statistics in the machine learning community is fairly recent. Their main attraction is the fact that they offer highly flexible models and their complexity scales appropriately with the amount of available data. In recent years, research on Bayesian nonparametric learning methods have focused on three main aspects: the construction of new models, the development of inference algorithms and new applications. This thesis presents our contributions to these three topics of research in the context of learning latent variables models. Firstly, we introduce the Pitman-Yor process mixture of Gaussians, a model for learning infinite mixtures of Gaussians. We also present an inference algorithm to discover the latent components of the model and we evaluate it on two practical robotics applications. Our results demonstrate that the proposed approach outperforms, both in performance and flexibility, the traditional learning approaches. Secondly, we propose the extended cascading Indian buffet process, a Bayesian nonparametric probability distribution on the space of directed acyclic graphs. In the context of Bayesian networks, this prior is used to identify the presence of latent variables and the network structure among them. A Markov Chain Monte Carlo inference algorithm is presented and evaluated on structure identification problems and as well as density estimation problems. Lastly, we propose the Indian chefs process, a model more general than the extended cascading Indian buffet process for learning graphs and orders. The advantage of the new model is that it accepts connections among observable variables and it takes into account the order of the variables. We also present a reversible jump Markov Chain Monte Carlo inference algorithm which jointly learns graphs and orders. Experiments are conducted on density estimation problems and testing independence hypotheses. This model is the first Bayesian nonparametric model capable of learning Bayesian learning networks with completely arbitrary graph structures.
Książki na temat "Latent"
Langeheine, Rolf, i Jürgen Rost, red. Latent Trait and Latent Class Models. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4757-5644-9.
Pełny tekst źródłaRolf, Langeheine, Rost Jürgen i Universität Kiel. Institut für die Pädagogik der Naturwissenschaften., red. Latent trait and latent class models. New York: Plenum Press, 1988.
Znajdź pełny tekst źródłaCollins, Linda M. Latent class and latent transition analysis. Hoboken, N.J: Wiley, 2010.
Znajdź pełny tekst źródłaA, Marcoulides George, i Moustaki Irini, red. Latent variable and latent structure models. Mahwah, N.J: Lawrence Earlbaum Publishers, 2002.
Znajdź pełny tekst źródłaLubow, R. E., i Ina Weiner, red. Latent Inhibition. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511730184.
Pełny tekst źródłaForrellad, Luisa. Foc latent. Barcelona: Angle Editorial, 2006.
Znajdź pełny tekst źródłaBarbu, Ion. Nadir latent. Bucarest: Minerva, 1985.
Znajdź pełny tekst źródłaRoyal Institute of British Architects., red. Latent defects. London: RIBA, 1990.
Znajdź pełny tekst źródłaFrühwirth, Michaela. Latent image. Amsterdam: Roma Publications, 2016.
Znajdź pełny tekst źródłaGhasi, Samuel. Latent error. Enugu, Nigeria: Rhyce Kerex Publishers, 2016.
Znajdź pełny tekst źródłaCzęści książek na temat "Latent"
Herwig, Heinz. "Latente Wärme (latent heat)". W Wärmeübertragung A-Z, 137–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-56940-1_32.
Pełny tekst źródłaFlaherty, Brian P., i Cara J. Kiff. "Latent class and latent profile models." W APA handbook of research methods in psychology, Vol 3: Data analysis and research publication., 391–404. Washington: American Psychological Association, 2012. http://dx.doi.org/10.1037/13621-019.
Pełny tekst źródłaMiller, Laura T., Lionel Stange, Charles MacVean, Jorge R. Rey, J. H. Frank, R. F. Mizell, John B. Heppner i in. "Latent Infection". W Encyclopedia of Entomology, 2142. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1969.
Pełny tekst źródłaMiller, Laura T., Lionel Stange, Charles MacVean, Jorge R. Rey, J. H. Frank, R. F. Mizell, John B. Heppner i in. "Latent Learning". W Encyclopedia of Entomology, 2142. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_1970.
Pełny tekst źródłaFernandes, Rodney A. "Latent Functionality". W Protecting-Group-Free Organic Synthesis, 229–57. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119295266.ch9.
Pełny tekst źródłaFischer, Gabriele, Annemarie Unger, W. Wolfgang Fleischhacker, Cécile Viollet, Jacques Epelbaum, Daniel Hoyer, Ina Weiner i in. "Latent Inhibition". W Encyclopedia of Psychopharmacology, 686–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_344.
Pełny tekst źródłaPierrehumbert, Ray. "Latent Heat". W Encyclopedia of Astrobiology, 913. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11274-4_130.
Pełny tekst źródłaKramer, Oliver. "Latent Sorting". W Dimensionality Reduction with Unsupervised Nearest Neighbors, 55–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38652-7_5.
Pełny tekst źródłaFranzen, Michael D. "Latent Variable". W Encyclopedia of Clinical Neuropsychology, 1434. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-79948-3_1212.
Pełny tekst źródłaMalik, Jamil A., Theresa A. Morgan, Falk Kiefer, Mustafa Al’Absi, Anna C. Phillips, Patricia Cristine Heyn, Katherine S. Hall i in. "Latent Variable". W Encyclopedia of Behavioral Medicine, 1145–47. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_758.
Pełny tekst źródłaStreszczenia konferencji na temat "Latent"
Sankaran, Anush, Tejas I. Dhamecha, Mayank Vatsa i Richa Singh. "On matching latent to latent fingerprints". W 2011 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2011. http://dx.doi.org/10.1109/ijcb.2011.6117525.
Pełny tekst źródła"Latent Ambiguity in Latent Semantic Analysis?" W International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004178301150120.
Pełny tekst źródłaNakjai, Pisit, Jiradej Ponsawat i Tatpong Katanyukul. "Latent cognizance". W the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3357254.3357266.
Pełny tekst źródłaKorman, Simon, i Roee Litman. "Latent RANSAC". W 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00700.
Pełny tekst źródłaAgarwal, Deepak, i Bee-Chung Chen. "Latent OLAP". W the 2011 international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1989323.1989415.
Pełny tekst źródłaBeutel, Alex, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto i Ed H. Chi. "Latent Cross". W WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159652.3159727.
Pełny tekst źródłaAnadol, Refik. "Latent History". W MM '19: The 27th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3343031.3355700.
Pełny tekst źródłaHu, Bo. "Generalized Latent Interdependence Model and Latent Nonindependence Model". W 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1577529.
Pełny tekst źródłaKoltcov, Sergei, Olessia Koltsova i Sergey Nikolenko. "Latent dirichlet allocation". W the 2014 ACM conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2615569.2615680.
Pełny tekst źródłaPasternack, Jeff, i Dan Roth. "Latent credibility analysis". W the 22nd international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2488388.2488476.
Pełny tekst źródłaRaporty organizacyjne na temat "Latent"
Henn, Soeren, i James Robinson. Africa's Latent Assets. Cambridge, MA: National Bureau of Economic Research, marzec 2021. http://dx.doi.org/10.3386/w28603.
Pełny tekst źródłaGrinfeld, Michael A., i Steven B. Segletes. Latent Work and Latent Heat of the Liquid/Vapor Transformation. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2014. http://dx.doi.org/10.21236/ada608749.
Pełny tekst źródłaTaylor, Melissa, Will Chapman, Austin Hicklin, George Kiebuzinski, John Mayer-Splain, Rachel Wallner i Peter Komarinski. Latent Interoperability Transmission Specification. National Institute of Standards and Technology, styczeń 2013. http://dx.doi.org/10.6028/nist.sp.1152.
Pełny tekst źródłaGarris, Michael D. Latent fingerprint training with NIST special database 27 and universal latent workstation. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6799.
Pełny tekst źródłaMassague, Joan. Dissecting and Targeting Latent Metastasis. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2013. http://dx.doi.org/10.21236/ada605186.
Pełny tekst źródłaLee, Paul, Haiying Guan, Andrew Dienstfrey, Mary Theofanos, Brian Stanton i Matthew T. Schwarz. Forensic latent fingerprint preprocessing assessment. Gaithersburg, MD: National Institute of Standards and Technology, czerwiec 2018. http://dx.doi.org/10.6028/nist.ir.8215.
Pełny tekst źródłaLettau, Martin, i Markus Pelger. Estimating Latent Asset-Pricing Factors. Cambridge, MA: National Bureau of Economic Research, maj 2018. http://dx.doi.org/10.3386/w24618.
Pełny tekst źródłaBonhomme, Stéphane, i Manuel Arellano. Recovering Latent Variables by Matching. The IFS, styczeń 2020. http://dx.doi.org/10.1920/wp.cem.2020.220.
Pełny tekst źródłaMeagher, S., i V. Dvornychenko. Defining AFIS latent print "Lights-Out. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7811.
Pełny tekst źródłaSchennach, Susanne M. Entropic Latent Variable Integration via Simulation. Cemmap, lipiec 2013. http://dx.doi.org/10.1920/wp.cem.2013.3213.
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