Relevant bibliographies by topics / Scoring methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Scoring methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Scoring methods"

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Tugwell, Peter, and J. André Knottnerus. "Scoring of methods indices." Journal of Clinical Epidemiology 101 (September 2018): vi—viii. http://dx.doi.org/10.1016/j.jclinepi.2018.08.001.

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Slikker, Marco, Peter Borm, and René van den Brink. "Internal slackening scoring methods." Theory and Decision 72, no. 4 (2011): 445–62. http://dx.doi.org/10.1007/s11238-011-9281-4.

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CETIN, Bayram, Nese GULER, and Rabia SARICA. "Using Generalizability Theory to Examine Different Concept Map Scoring Methods." Eurasian Journal of Educational Research 16, no. 66 (2016): 1–30. http://dx.doi.org/10.14689/ejer.2016.66.12.

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Zurada, Jozef. "Rule Induction Methods For Credit Scoring." Review of Business Information Systems (RBIS) 11, no. 2 (2007): 11–22. http://dx.doi.org/10.19030/rbis.v11i2.4421.

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Baraliakos, Xenofon, and Juergen Braun. "Imaging Scoring Methods in Axial Spondyloarthritis." Rheumatic Disease Clinics of North America 42, no. 4 (2016): 663–78. http://dx.doi.org/10.1016/j.rdc.2016.07.006.

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Copas, J. B. "The Shrinkage of Point Scoring Methods." Applied Statistics 42, no. 2 (1993): 315. http://dx.doi.org/10.2307/2986235.

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Faulkner, K., and J. Law. "Methods of Scoring Mammographic Phantom Films." Radiation Protection Dosimetry 49, no. 1-3 (1993): 183–85. http://dx.doi.org/10.1093/rpd/49.1-3.183.

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Chan, Alicia K. J., Joanne M. Langley, and John C. LeBlanc. "Validation of croup scoring methods 694." Pediatric Research 41 (April 1997): 118. http://dx.doi.org/10.1203/00006450-199704001-00714.

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Noseworthy, John H. "Clinical scoring methods for multiple sclerosis." Annals of Neurology 36, S1 (1994): S80—S85. http://dx.doi.org/10.1002/ana.410360718.

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Faulkner, K., and J. Law. "Methods of Scoring Mammographic Phantom Films." Radiation Protection Dosimetry 49, no. 1-3 (1993): 183–85. http://dx.doi.org/10.1093/oxfordjournals.rpd.a081932.

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Dissertations / Theses on the topic "Scoring methods"

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Iscanoglu, Aysegul. "Credit Scoring Methods And Accuracy Ratio." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606502/index.pdf.

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The credit scoring with the help of classification techniques provides to take easy and quick decisions in lending. However, no definite consensus has been reached with regard to the best method for credit scoring and in what conditions the methods performs best. Although a huge range of classification techniques has been used in this area, the logistic regression has been seen an important tool and used very widely in studies. This study aims to examine accuracy and bias properties in parameter estimation of the logistic regression by using Monte Carlo simulations in four aspect which are dimension of the sets, length, the included percentage defaults in data and effect of variables on estimation. Moreover, application of some important statistical and non-statistical methods on Turkish credit default data is provided and the method accuracies are compared for Turkish market. Finally, ratings on the results of best method is done by using receiver operating characteristic curve.
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Leos, Leonard Rouleau Paul Wadsworth Mark. "Budget scoring of alternative financing methods for defense requirements." Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FLeos%5FMBA.pdf.

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"Submitted in partial fulfillment of the requirements for the degree of Master of Business Administration from the Naval Postgraduate School, June 2007."<br>Advisor(s): Summers, Donald ; San Miguel, Joseph. "June 2007." "MBA professional report"--Cover. Description based on title screen as viewed on August 15, 2007. Includes bibliographical references (p. 37-38). Also available in print.
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Leos, Leonard, Mark Wadsworth, and Paul Rouleau. "Budget scoring of alternative financing methods for defense requirements." Monterey California. Naval Postgraduate School, 2007. http://hdl.handle.net/10945/10247.

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MBA Professional Report<br>This research investigates procurement scoring and the Department of Defense's (DoD) use of alternative financing methods, such as leases and public-private ventures. One of the major impediments to using alternative forms of procurement financing for acquiring defense capabilities is in the budgetary treatment, or "scoring," of these initiatives by the Congressional Budget Office (CBO), the Office of Management and Budget (OMB) and the congressional Budget Committees. The current scoring policy that has been applied to many initiatives essentially negates the financial advantage for using alternative forms of financing. Therefore, this research examines existing policies and their adherence to statutes and the role of the various government organizations and committees in actual recording of obligations and outlays related to financing alternatives used by federal agencies. Preliminary evidence suggests that this emerging area has major importance for future DoD acquisitions in a resource-constrained environment. Included are recommendations for changes in budgetary scoring that encompass the full scope of federal obligations and expenditures while promoting efficient, more rapid and fiscally responsible acquisitions.
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Stepanova, Maria. "Using survival analysis methods to build credit scoring models." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364729.

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Ramsay, Mark J. "Comparing Five Empirical Biodata Scoring Methods for Personnel Selection." Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3220/.

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A biodata based personnel selection measure was created to improve the retention rate of Catalog Telemarketing Representatives at a major U.S. retail company. Five separate empirical biodata scoring methods were compared to examine their usefulness in predicting retention and reducing adverse impact. The Mean Standardized Criterion Method, the Option Criterion Correlation Method, Horizontal Percentage Method, Vertical Percentage Method, and Weighted Application Blank Method using England's (1971) Assigned Weights were employed. The study showed that when using generalizable biodata items, all methods, except the Weighted Application Blank Method, were similar in their ability to discriminate between low and high retention employees and produced similar low adverse impact effects. The Weighted Application Blank Method did not discriminate between the low and high retention employees.
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Fenu, Luca Antonio. "The Development of Novel Scoring Methods for Virtual Screening." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485039.

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Protein ligand docking is a valuable technique in the field of Structure Based Drug Design. One of its applications is to select one orfew candidates from a set of thousands or million compounds, often mmm as Virntal Screening. The choice of the correct scoring function is of paramount importance, and currently available scoring function are most often implemented to optimise reproduction of monn binding modes, or experimental free energies. The vast amount of information contained in the fact that most ligand do not, indeed bind to a given protein is discarded, building a considerable bias in these functions. The parameterisation method proposed in this work attempt to overcome said issue, by using a set ofdecoys among which the mown ligand is hidden. The parameters ofthe scoring function are chosen by a Genetic Algorithm so to maximize the monn ligand' ranking. The generality of the ftlnction is maintained by the constraint of simultaneously optimising several of these sets: the influence of the training set's size on the quality and robustness of the flUlction is assessed. Also, the effect of using different treference poses for the mown ligand, either experimental or re-generated by docking, is investigated. Finally, the immediate. applicability of the method is shown by reparameterising commercially available scoring functions (GOWSCORE, CHEMSCORE); a final attempt is made at creating scoringfimctions tailored to specific proteinfamilies', interpreting the results in tenllS ofwhat is mmm of their ligan-protein interactions. TIle results show that such a function is able to discriminate between mown actives and decoys, and would therefore be a valuable addition to the computational chemist bag of tricks to select those molecules able to tum into successfill drugs.
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Francisco, Jon S. Von Krueger Kristopher E. "An evaluation of Yuma Proving Grounds ballistic arsenal scoring methods /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Jun%5FFrancisco.pdf.

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Thesis (M.S. in Applied Science (Operations Research))--Naval Postgraduate School, June 2005.<br>Thesis Advisor(s): Lyn R. Whitaker. Includes bibliographical references (p. 113). Also available online.
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Kraus, Anne. "Recent methods from statistics and machine learning for credit scoring." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-171439.

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Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion. The logistic regression model is the most widely used method for creating credit scorecards and classifying applicants into risk classes. Since this development process, based on the logit model, is standard in the retail banking practice, the predictive accuracy of this proceeding is used for benchmark reasons throughout this thesis. The AUC approach is a main task introduced within this work. Instead of using the maximum likelihood estimation, the AUC is considered as objective function to optimize it directly. The coefficients are estimated by calculating the AUC measure with Wilcoxon-Mann-Whitney and by using the Nelder-Mead algorithm for the optimization. The AUC optimization denotes a distribution-free approach, which is analyzed within a simulation study for investigating the theoretical considerations. It can be shown that the approach still works even if the underlying distribution is not logistic. In addition to the AUC approach and classical well-known methods like generalized additive models, new methods from statistics and machine learning are evaluated for the credit scoring case. Conditional inference trees, model-based recursive partitioning methods and random forests are presented as recursive partitioning algorithms. Boosting algorithms are also explored by additionally using the AUC as a loss function. The empirical evaluation is based on data from a German bank. From the application scoring, 26 attributes are included in the analysis. Besides the AUC, different performance measures are used for evaluating the predictive performance of scoring models. While classification trees cannot improve predictive accuracy for the current credit scoring case, the AUC approach and special boosting methods provide outperforming results compared to the robust classical scoring models regarding the predictive performance with the AUC measure.<br>Scoringmodelle dienen Finanzinstituten als Grundlage dafür, die Ausfallwahrscheinlichkeit von Kreditantragstellern zu berechnen und zu entscheiden ob ein Kredit gewährt wird oder nicht. Das AUC (area under the receiver operating characteristic curve) ist eines der am häufigsten verwendeten Maße, um die Vorhersagekraft im Kreditscoring zu bewerten. Demzufolge besteht das Ziel dieser Arbeit darin, verschiedene Methoden zur Scoremodell-Bildung hinsichtlich eines optimierten AUC Maßes zu „benchmarken“. Während das genannte Maß dazu dient die vorgestellten Algorithmen hinsichtlich ihrer Trennschärfe zu bewerten, wird das AUC insbesondere als direktes Optimierungskriterium eingeführt. Die logistische Regression ist das am häufigsten verwendete Verfahren zur Entwicklung von Scorekarten und die Einteilung der Antragsteller in Risikoklassen. Da der Entwicklungsprozess mittels logistischer Regression im Retail-Bankenbereich stark etabliert ist, wird die Trennschärfe dieses Verfahrens in der vorliegenden Arbeit als Benchmark verwendet. Der AUC Ansatz wird als entscheidender Teil dieser Arbeit vorgestellt. Anstatt die Maximum Likelihood Schätzung zu verwenden, wird das AUC als direkte Zielfunktion zur Optimierung verwendet. Die Koeffizienten werden geschätzt, indem für die Berechnung des AUC die Wilcoxon Statistik und für die Optimierung der Nelder-Mead Algorithmus verwendet wird. Die AUC Optimierung stellt einen verteilungsfreien Ansatz dar, der im Rahmen einer Simulationsstudie untersucht wird, um die theoretischen Überlegungen zu analysieren. Es kann gezeigt werden, dass der Ansatz auch dann funktioniert, wenn in den Daten kein logistischer Zusammenhang vorliegt. Zusätzlich zum AUC Ansatz und bekannten Methoden wie Generalisierten Additiven Modellen, werden neue Methoden aus der Statistik und dem Machine Learning für das Kreditscoring evaluiert. Klassifikationsbäume, Modell-basierte Recursive Partitioning Methoden und Random Forests werden als Recursive Paritioning Methoden vorgestellt. Darüberhinaus werden Boosting Algorithmen untersucht, die auch das AUC Maß als Verlustfunktion verwenden. Die empirische Analyse basiert auf Daten einer deutschen Kreditbank. 26 Variablen werden im Rahmen der Analyse untersucht. Neben dem AUC Maß werden verschiedene Performancemaße verwendet, um die Trennschärfe von Scoringmodellen zu bewerten. Während Klassifikationsbäume im vorliegenden Kreditscoring Fall keine Verbesserungen erzielen, weisen der AUC Ansatz und einige Boosting Verfahren gute Ergebnisse im Vergleich zum robusten klassischen Scoringmodell hinsichtlich des AUC Maßes auf.
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Von, Krueger Kristopher E. "An evaluation of Yuma Proving Grounds ballistic arsenal scoring methods." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/2153.

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Yuma Test Center (YTC) is a primary testing and evaluation facility for the United States Army's 30mm M230 automatic gun. The current program used by YTC to evaluate 30mm rounds is an old, complex, FORTRAN package called Accuracy Scoring Program (ASCORE). Due to the age and complexity of ASCORE, questions about its reliability in ballistic calculations have been raised. These reliability questions are important, because ASCORE is used by YTC to evaluate whether all such weapons systems meet Department of Defense specifications. Currently, the Army has no plans to replace ASCORE. The Navy has expressed interest in using ASCORE for fire control We evaluate ASCORE using a modern ballistic simulator named Projectile Design and Analysis System (PRODAS) to simulate trajectories of live fire rounds. This thesis demonstrates how PRODAS can be used to evaluate ASCORE. Preliminary results indicate that ASCORE's accuracy decreases the greater a round's impact distance is from the target.
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Rapp-Olsson, Malin Elisabeth. "Investigating the Success of Scoring Methods When Faced With Manipulative Judging." Thesis, The University of Arizona, 2013. http://hdl.handle.net/10150/311802.

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Books on the topic "Scoring methods"

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Przuntek, Horst, Peter H. Kraus, P. Klotz, and Amos D. Korczyn, eds. Instrumental Methods and Scoring in Extrapyramidal Disorders. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-78914-4.

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Scoring performance assessments based on judgements: Generalizability theory. Kluwer Academic Publishers, 2001.

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Green, Bert F. Fixed-weight methods of scoring computer-based adaptive tests. Law School Admission Council, 2002.

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Green, Bert F. Fixed-weight methods of scoring computer-based adaptive tests. Law School Admission Council, 2002.

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Cope, Ronald Thomas. A study of methods for estimating distributions of test scores. American College Testing Program, 1990.

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Hanson, Bradley A. An investigation of methods for improving estimation of test score distributions. American College Testing Program, 1990.

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L, Brennan Robert, ed. Test equating: Methods and practices. Springer-Verlag, 1995.

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Hanson, Bradley A. A comparison of presmoothing and postsmoothing methods in equipercentile equating. American College Testing Program, 1994.

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Marco, Gary L. Methods used to establish score comparability on the enhanced ACT assessment and the SAT. College Entrance Examination Board, 1992.

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Kolen, Michael J. Test equating, scaling, and linking: Methods and practices. Springer, 2014.

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Book chapters on the topic "Scoring methods"

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Haghighat, Alireza. "Scoring/Tallying." In Monte Carlo Methods for Particle Transport. CRC Press, 2020. http://dx.doi.org/10.1201/9780429198397-8.

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Flores-Rentería, Lluvia, and Andrew Krohn. "Scoring Microsatellite Loci." In Methods in Molecular Biology. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-389-3_21.

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Carey, Allison F., Robert Ménard, and Daniel Y. Bargieri. "Scoring Sporozoite Motility." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-026-7_26.

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Laslier, Jean-François. "Scoring and Ranking Methods." In Studies in Economic Theory. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60805-6_4.

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Hill, Anthony D., and Peter J. Reilly. "Scoring Functions for AutoDock." In Methods in Molecular Biology. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2343-4_27.

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Müller, Marlene, and Bernd Rönz. "Credit Scoring using Semiparametric Methods." In Measuring Risk in Complex Stochastic Systems. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1214-0_5.

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Lange, Kenneth. "Newton’s Method and Scoring." In Mathematical and Statistical Methods for Genetic Analysis. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_3.

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Lange, Kenneth. "Newton’s Method and Scoring." In Mathematical and Statistical Methods for Genetic Analysis. Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5_3.

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Bitencourt-Ferreira, Gabriela, and Walter Filgueira de Azevedo. "Exploring the Scoring Function Space." In Methods in Molecular Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9752-7_17.

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Mele, Tina S. "Scoring Sepsis Severity in Mice." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1488-4_13.

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Conference papers on the topic "Scoring methods"

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Sze, Kam-Heung, Zhiqiang Xiong, Jinlong Ma, Gang Lu, Wai-Yee Chan, and Hongjian Li. "Influence of Data Similarity on the Scoring Power of Machine-learning Scoring Functions for Docking." In 11th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008873800850092.

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Taghipour, Kaveh, and Hwee Tou Ng. "A Neural Approach to Automated Essay Scoring." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1193.

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Paramonov, Pavel, and Nadezhda Sutula. "Simplified Scoring Methods in HMM Based Speech Recognition." In 2014 International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2014. http://dx.doi.org/10.1109/iscmi.2014.32.

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Ingalls, Ricki G., and Douglas J. Morrice. "Alternative thread scoring methods in qualitative event graphs." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419660.

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Pribadi, Feddy Setio, Teguh Bharata Adji, Adhistya Erna Permanasari, Anggraini Mulwinda, and Aryo Baskoro Utomo. "Automatic short answer scoring using words overlapping methods." In ENGINEERING INTERNATIONAL CONFERENCE (EIC) 2016: Proceedings of the 5th International Conference on Education, Concept, and Application of Green Technology. Author(s), 2017. http://dx.doi.org/10.1063/1.4976906.

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Billiet, Lieven, Sabine Van Huffel, and Vanya Van Belle. "Interval Coded Scoring Index with Interaction Effects - A Sensitivity Study." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005646500330040.

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Lee, Sungjin. "Online Sentence Novelty Scoring for Topical Document Streams." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1067.

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Dong, Fei, and Yue Zhang. "Automatic Features for Essay Scoring – An Empirical Study." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1115.

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Cane, John W. "Scoring IMS Regattas - An Empirical Study of Alternative Methods." In SNAME 12th Chesapeake Sailing Yacht Symposium. SNAME, 1995. http://dx.doi.org/10.5957/csys-1995-006.

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The International Measurement System (IMS) uses a computerized velocity prediction program (VPP) to calculate the performance of a meas­ured hull and rig in winds from six to twenty knots, at any sailing angle. A regatta is scored by comparing a yacht's performance with pre­dictions of the VPP. The winner is the yacht whose performance, relative to its VPP predic­tions, is the best, compared to all other yachts in its class or division. This paper discusses different methods of malc­ing the comparison and accounting for various factors in the race such as wind shifts and cur­rent on the course. Decisions made by race man­agers and/or developers of scoring programs can significantly impact results. Illustrative examples show the effects that these decisions can have. In 1994 the number of data points available for use in scoring yachts in custom courses doubled. Alternative ways of using these data are illus­trated by application to a sample regatta.
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Prochazka, Ales, Oldrich Vysata, Jri Kuchynka, Delaram Jarchi, and Saeid Sanei. "Comparative Study of Deep Learning Based Sleep Scoring Methods." In 2020 3rd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM). IEEE, 2020. http://dx.doi.org/10.1109/elecom49001.2020.9296996.

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Reports on the topic "Scoring methods"

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Griffin, G. R., and J. D. Mosko. A Comparison of Dichotic Listening Task Scoring Methods. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada159920.

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Dong, Cheryl. A comparative study of three language sampling methods using developmental sentence scoring. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.5473.

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Buck, J. W., G. M. Gelston, and W. T. Farris. Scoring methods and results for qualitative evaluation of public health impacts from the Hanford high-level waste tanks. Integrated Risk Assessment Program. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/115734.

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NREL Develops Method to Evaluate Accuracy of Home Energy Scoring Tool (Fact Sheet). Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1079710.

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