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Relevant bibliographies by topics / Least

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Academic literature on the topic 'Least'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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Journal articles on the topic "Least"

1

AL-CHALABI, M. "WHEN LEAST-SQUARES SQUARES LEAST1." Geophysical Prospecting 40, no. 3 (1992): 359–78. http://dx.doi.org/10.1111/j.1365-2478.1992.tb00380.x.

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Petras, Ivo, and Igor Podlubny. "Least Squares or Least Circles?" CHANCE 23, no. 2 (2010): 38–42. http://dx.doi.org/10.1080/09332480.2010.10739804.

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Petras, Ivo, and Igor Podlubny. "Least squares or least circles?" CHANCE 23, no. 2 (2010): 38–42. http://dx.doi.org/10.1007/s00144-010-0021-2.

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Jeníček, V., and Š. Grófová. "Least developed countries – characteristics." Agricultural Economics (Zemědělská ekonomika) 60, No. 2 (2014): 65–73. http://dx.doi.org/10.17221/67/2013-agricecon.

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Despite some progresses in the political situation, the effects of the economic crisis and widespread food insecurity are expected to persist. Humanitarian assistance is now needed to support the needs of the most vulnerable. In the longer term, countries have the significant goal of consolidating peace and security and strengthening the overall governance, while at the same time, reconstructing and rehabilitating their economy. &nbsp;

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Jeníček, V., and Š. Grófová. "Least developed countries – comparison." Agricultural Economics (Zemědělská ekonomika) 60, No. 3 (2014): 99–109. http://dx.doi.org/10.17221/70/2013-agricecon.

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The inter-related causes of food insecurity in these countries are mainly the long lasting civil wars, a limited access to land, environmental degradation, climatic shocks and the rapid population growth resulting from the high birth rates and the return of refugees. &nbsp; &nbsp;

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Cvetkovic, Dragos, and Dragan Stevanovic. "Graphs with least eigenvalue at least -√3." Publications de l'Institut Mathematique 73, no. 87 (2003): 39–51. http://dx.doi.org/10.2298/pim0373039c.

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Bassett, C. Dylan. "At Least." Dialogue: A Journal of Mormon Thought 50, no. 4 (2017): 105–7. http://dx.doi.org/10.5406/dialjmormthou.50.4.0105.

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Kaufman, Asher S. "Least action." Physics World 18, no. 2 (2005): 21. http://dx.doi.org/10.1088/2058-7058/18/2/31.

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Kogan, Naomi R. "At Least." Palliative and Supportive Care 11, no. 2 (2012): 175. http://dx.doi.org/10.1017/s1478951512000041.

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Fearn, Tom. "Least Squares." NIR news 10, no. 1 (1999): 7–13. http://dx.doi.org/10.1255/nirn.502.

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Dissertations / Theses on the topic "Least"

1

Jones, Caroline Erin. "Least squares Gaussian quadrature." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0017/MQ54628.pdf.

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Hassel, Per Anker. "Nonlinear partial least squares." Thesis, University of Newcastle Upon Tyne, 2003. http://hdl.handle.net/10443/465.

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Partial Least Squares (PLS) has been shown to be a versatile regression technique with an increasing number of applications in the areas of process control, process monitoring and process analysis. This Thesis considers the area of nonlinear PLS; a nonlinear projection based regression technique. The nonlinearity is introduced as a univariate nonlinear function between projections, or to be more specific, linear combinations of the predictor and the response variables. As for the linear case, the method should handle multicollinearity, underdetermined and noisy systems. Although linear PLS is accepted as an empirical regression method, none of the published nonlinear PLS algorithms have achieved widespread acceptance. This is confirmed from a literature survey where few real applications of the methodology were found. This Thesis investigates two nonlinear PLS methodologies, in particular focusing on their limitations. Based on these studies, two nonlinear PLS algorithms are proposed. In the first of the two existing approaches investigated, the projections are updated by applying an optimization method to reduce the error of the nonlinear inner mapping. This ensures that the error introduced by the nonlinear inner mapping is minimized. However, the procedure is limited as a consequence of problems with the nonlinear optimisation. A new algorithm, Nested PLS (NPLS), is developed to address these issues. In particular, a separate inner PLS is used to update the projections. The NPLS algorithm is shown to outperform existing algorithms for a wide range of regression problems and has the potential to become a more widely accepted nonlinear PLS algorithm than those currently reported in the literature. In the second of the existing approaches, the projections are identified by examining each variable independently, as opposed to minimizing the error of the nonlinear inner mapping directly. Although the approach does not necessary identify the underlying functional relationship, the problems of overfitting and other problems associated with optimization are reduced. Since the underlying functional relationship may not be established accurately, the reliability of the nonlinear inner mapping will be reduced. To address this problem a new algorithm, the Reciprocal Variance PLS (RVPLS), is proposed. Compared with established methodology, RVPLS focus more on finding the underlying structure, thus reducing the difficulty of finding an appropriate inner mapping. RVPLS is shown to perform well for a number of applications, but does not have the wide-ranging performance of Nested PLS.

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Ganssle, Graham. "Stabilized Least Squares Migration." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2074.

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Before raw seismic data records are interpretable by geologists, geophysicists must process these data using a technique called migration. Migration spatially repositions the acoustic energy in a seismic record to its correct location in the subsurface. Traditional migration techniques used a transpose approximation to a true acoustic propagation operator. Conventional least squares migration uses a true inverse operator, but is limited in functionality by the large size of modern seismic datasets. This research uses a new technique, called stabilized least squares migration, to correctly migrate seismic data records using a true inverse operator. Contrary to conventional least squares migration, this new technique allows for errors over ten percent in the underlying subsurface velocity model, which is a large limitation in conventional least squares migration. The stabilized least squares migration also decreases the number of iterations required by conventional least squares migration algorithms by an average of about three iterations on the sample data tested in this research.

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Young, William Ronald. "Total least squares and constrained least squares applied to frequency domain system identification." Ohio : Ohio University, 1993. http://www.ohiolink.edu/etd/view.cgi?ohiou1176315127.

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Guo, Hengdao. "Frequency Tracking and Phasor Estimation Using Least Squares and Total Least Squares Algorithms." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/57.

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System stability plays an important role in electric power systems. With the development of electric power system, the scale of the electric grid is now becoming larger and larger, and many renewable energy resources are integrated in the grid. However, at the same time, the stability and safety issues of electric power system are becoming more complicated. Frequency and phasors are two critical parameters of the system stability. Obtaining these two parameters have been great challenges for decades. Researchers have provided various kinds of algorithms for frequency tracking and phasor estimation. Among them, Least Squares (LS) algorithm is one of the most commonly used algorithm. This thesis studies the LS algorithm and the Total Least Squares (TLS) algorithm working on frequency tracking and phasor estimation. In order to test the performance of the two algorithms, some simulations have been made in the Matlab. The Total Vector Error (TVE) is a commonly used performance criteria, and the TVE results of the two algorithms are compared. The TLS algorithm performs better than LS algorithm when the frequencies of all harmonic components are given.

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Santiago, Claudio Prata. "On the nonnegative least squares." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31768.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Earl Barnes; Committee Member: Arkadi Nemirovski; Committee Member: Faiz Al-Khayyal; Committee Member: Guillermo H. Goldsztein; Committee Member: Joel Sokol. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Müller, Werner. "On Least Squares Variogram Fitting." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1997. http://epub.wu.ac.at/370/1/document.pdf.

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Yao, Gang. "Least-squares reverse-time migration." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/14575.

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Conventional migration methods, including reverse-time migration (RTM) have two weaknesses: first, they use the adjoint of forward-modelling operators, and second, they usually apply a crosscorrelation imaging condition to extract images from reconstructed wavefields. Adjoint operators, which are an approximation to inverse operators, can only correctly calculate traveltimes (phase), but not amplitudes. To preserve the true amplitudes of migration images, it is necessary to apply the inverse of the forward-modelling operator. Similarly, crosscorrelation imaging conditions also only correct traveltimes (phase) but do not preserve amplitudes. Besides, the examples show crosscorrelation imaging conditions produce strong sidelobes. Least-squares migration (LSM) uses both inverse operators and deconvolution imaging conditions. As a result, LSM resolves both problems in conventional migration methods and produces images with fewer artefacts, higher resolution and more accurate amplitudes. At the same time, RTM can accurately handle all dips, frequencies and any type of velocity variation. Combining RTM and LSM produces least-squares reverse-time migration (LSRTM), which in turn has all the advantages of RTM and LSM. In this thesis, we implement two types of LSRTM: matrix-based LSRTM (MLSRTM) and non-linear LSRTM (NLLSRTM). MLSRTM is a matrix formulation of LSRTM and is more stable than conventional LSRTM; it can be implemented with linear inversion algorithms but needs a large amount of computer memory. NLLSRTM, by contrast, directly expresses migration as an optimisation which minimises the 2 norm of the residual between the predicted and observed data. NLLSRTM can be implemented using non-linear gradient inversion algorithms, such as non-linear steepest descent and non-linear conjugated-gradient solvers. We demonstrate that both MLSRTM and NLLSRTM can achieve better images with fewer artefacts, higher resolution and more accurate amplitudes than RTM using three synthetic examples. The power of LSRTM is also further illustrated using a field dataset. Finally, a simple synthetic test demonstrates that the objective function used in LSRTM is sensitive to errors in the migration velocity. As a result, it may be possible to use NLLSRTM to both refine the migrated image and estimate the migration velocity.

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Kim, Donggeon. "Least squares mixture decomposition estimation." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02132009-171622/.

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Dugger, Katie M. "Foraging ecology and reproductive success of Least terns nesting on the lower Mississippi River /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841279.

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Books on the topic "Least"

1

Least wanted. Renegade Press, 2011.

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David, Hamilton. The least hinge. Frith Press, 2002.

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(Group), Ugly Kid Joe. America's least wanted. Cherry Lane, 1993.

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The least one. Herald Press, 1989.

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Doing least harm. Brooding Heron Press, 1985.

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ill, Arnold Tim 1957, ed. Least of all. M.K. McElderry Books, 1987.

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ill, Arnold Tim 1957, ed. Least of all. Aladdin Books, 1993.

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Copyright Paperback Collection (Library of Congress), ed. Least likely wedding? Silhouette Books, 2005.

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Hinshaw, Victoria. Least likely lovers. Kensington Pub., 2005.

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Deal, Borden. The least one. University of Alabama Press, 1992.

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Book chapters on the topic "Least"

1

Paige, Christopher C., and Zdeněk Strakoš. "Unifying Least Squares, Total Least Squares and Data Least Squares." In Total Least Squares and Errors-in-Variables Modeling. Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-3552-0_3.

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Lyche, Tom. "Least Squares." In Numerical Linear Algebra and Matrix Factorizations. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36468-7_9.

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Rey Vega, Leonardo, and Hernan Rey. "Least Squares." In SpringerBriefs in Electrical and Computer Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30299-2_5.

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White, Halbert. "Least Squares." In Time Series and Statistics. Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20865-4_15.

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Sayfy, Ali. "Least Squares." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7163-9_149-1.

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Sen, Ashish, and Tony E. Smith. "Least Squares." In Advances in Spatial and Network Economics. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79880-1_7.

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Lyche, Tom, Georg Muntingh, and Øyvind Ryan. "Least Squares." In Texts in Computational Science and Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59789-4_9.

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Bornemann, Folkmar. "Least Squares." In Springer Undergraduate Mathematics Series. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74222-9_4.

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Haberman, Shelby J. "Least Squares." In Springer Series in Statistics. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-4417-0_5.

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De Capitani diVimercati, Sabrina. "Least Privilege." In Encyclopedia of Cryptography and Security. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_820.

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Conference papers on the topic "Least"

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Chemingui, N., F. Liu, and S. Lu. "Least-Squares Migration Beyond Primaries." In First EAGE/SBGf Workshop on Least-Squares Migration. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201803058.

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Liu, Z., and G. Schuster. "Neural Network Least Squares Migration." In First EAGE/SBGf Workshop on Least-Squares Migration. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201803061.

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Kotti, Afef, Rached Hamza, and Kamel Bouleimen. "Bandwidth Constrained Least Delay Least Cost Routing Algorithm." In EUROCON 2007 - The International Conference on "Computer as a Tool". IEEE, 2007. http://dx.doi.org/10.1109/eurcon.2007.4400497.

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DaI, W., X. Cheng, K. Jiao, and D. Vigh. "Iterative Least-squares Migration without Cycle Skipping." In First EAGE/SBGf Workshop on Least-Squares Migration. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201803064.

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Pereira-Dias, B., C. Guerra, A. Bulcão, and R. M. Dias. "Exploring Inversion Strategies in Image Domain Least Squares Migration." In First EAGE/SBGf Workshop on Least-Squares Migration. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201803060.

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Dose, V., and U. von Toussaint. "Beyond least squares." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819988.

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Karny, M. "Partitioned least squares." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940240.

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Qiao, Sanzheng. "Integer least squares." In the 2008 C3S2E conference. ACM Press, 2008. http://dx.doi.org/10.1145/1370256.1370261.

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Lunglmayr, Michael, Christoph Unterrieder, and Mario Huemer. "Approximate least squares." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854489.

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Care, Algo, Simone Garatti, and Marco C. Campi. "Least squares estimates and the coverage of least squares costs." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760841.

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Reports on the topic "Least"

1

Blaha, George. Nonlinear Parametric Least-Squares Adjustment. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada184039.

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DREWIEN, CELESTE A. A Parallel Prediction-Augmented Classical Least Squares/Partial Least Squares Hybrid Algorithm: CPLS 1.0 Code. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/759455.

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Li, Qin, and Les Atlas. Time-Variant Least Squares Harmonic Modeling. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada436659.

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Lippert, Ross, and Ryan Rifkin. Asymptotics of Gaussian Regularized Least-Squares. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada454981.

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Dennis, Jr, Wolkowicz J. E., and H. Sizing and Least Change Secant Methods. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada455266.

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Fraley, Christina. Algorithms for Nonlinear Least-Squares Problems. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada196071.

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Ruegg, Rosalie. Least-cost energy decisions for buildings:. National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.4309.

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Wolfe, Claire M. An interactive nonlinear least squares program. National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.tn.1238.

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Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for number restrictions or for existential restrictions. Many applications, however, require to combine these constructors. In this work, we present an lcs algorithm for the description logic ALEN, which allows for both constructors (as well as concept conjunction, primitive negation, and value restrictions). The proof of correctness of our lcs algorithm is based on an appropriate structural characterization of subsumption in ALEN also introduced in this paper.

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Küsters, Ralf, and Ralf Molitor. Computing Least Common Subsumers in ALEN. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.110.

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Computing the least common subsumer (lcs) in description logics is an inference task first introduced for sublanguages of CLASSIC. Roughly speaking, the lcs of a set of concept descriptions is the most specific concept description that subsumes all of the input descriptions. As such, the lcs allows to extract the commonalities from given concept descriptions, a task essential for several applications like, e.g., inductive learning, information retrieval, or the bottom-up construction of KR-knowledge bases. Previous work on the lcs has concentrated on description logics that either allow for number restrictions or for existential restrictions. Many applications, however, require to combine these constructors. In this work, we present an lcs algorithm for the description logic ALEN, which allows for both constructors (as well as concept conjunction, primitive negation, and value restrictions). The proof of correctness of our lcs algorithm is based on an appropriate structural characterization of subsumption in ALEN also introduced in this paper.

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