Готові списки джерел за темами / Comparison measures

Добірка наукової літератури з теми "Comparison measures"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Comparison measures"

1

Rendsburg, Luca, and Damien Garreau. "Comparison-based centrality measures." International Journal of Data Science and Analytics 11, no. 3 (April 2021): 243–59. http://dx.doi.org/10.1007/s41060-021-00254-4.

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AbstractRecently, learning only from ordinal information of the type “item x is closer to item y than to item z” has received increasing attention in the machine learning community. Such triplet comparisons are particularly well suited for learning from crowdsourced human intelligence tasks, in which workers make statements about the relative distances in a triplet of items. In this paper, we systematically investigate comparison-based centrality measures on triplets and theoretically analyze their underlying Euclidean notion of centrality. Two such measures already appear in the literature under opposing approaches, and we propose a third measure, which is a natural compromise between these two. We further discuss their relation to statistical depth functions, which comprise desirable properties for centrality measures, and conclude with experiments on real and synthetic datasets for medoid estimation and outlier detection.
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2

QU, Yanhui. "Comparison of fractal measures." Science in China Series A 48, no. 11 (2005): 1545. http://dx.doi.org/10.1360/04ys0211.

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3

Prasad, Somdutt. "Comparison of Prophylactic Measures." Journal of Cataract & Refractive Surgery 28, no. 12 (December 2002): 2071. http://dx.doi.org/10.1016/s0886-3350(02)01910-7.

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4

Tentori, Katya, Vincenzo Crupi, Nicolao Bonini, and Daniel Osherson. "Comparison of confirmation measures." Cognition 103, no. 1 (April 2007): 107–19. http://dx.doi.org/10.1016/j.cognition.2005.09.006.

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5

Ball, Fabian, and Andreas Geyer-Schulz. "Invariant Graph Partition Comparison Measures." Symmetry 10, no. 10 (October 15, 2018): 504. http://dx.doi.org/10.3390/sym10100504.

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Symmetric graphs have non-trivial automorphism groups. This article starts with the proof that all partition comparison measures we have found in the literature fail on symmetric graphs, because they are not invariant with regard to the graph automorphisms. By the construction of a pseudometric space of equivalence classes of permutations and with Hausdorff’s and von Neumann’s methods of constructing invariant measures on the space of equivalence classes, we design three different families of invariant measures, and we present two types of invariance proofs. Last, but not least, we provide algorithms for computing invariant partition comparison measures as pseudometrics on the partition space. When combining an invariant partition comparison measure with its classical counterpart, the decomposition of the measure into a structural difference and a difference contributed by the group automorphism is derived.
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6

Oyeka, I. C. A., and C. C. Nnanatu. "Pairwise Comparison in Repeated Measures." Journal of Modern Applied Statistical Methods 13, no. 2 (November 1, 2014): 151–68. http://dx.doi.org/10.22237/jmasm/1414814820.

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7

Laures, Jacqueline S., and Gary G. Weismer. "Comparison of speech intelligibility measures." Journal of the Acoustical Society of America 117, no. 4 (April 2005): 2604. http://dx.doi.org/10.1121/1.4777735.

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8

Eisert, Jens, and Martin B. Plenio. "A comparison of entanglement measures." Journal of Modern Optics 46, no. 1 (January 1999): 145–54. http://dx.doi.org/10.1080/09500349908231260.

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9

Maor, Yasmin, Miri King, Liraz Olmer, and Benjamin Mozes. "A comparison of three measures." Journal of Clinical Epidemiology 54, no. 6 (June 2001): 565–70. http://dx.doi.org/10.1016/s0895-4356(00)00338-3.

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10

Rebeschini, Patrick, and Ramon van Handel. "Comparison Theorems for Gibbs Measures." Journal of Statistical Physics 157, no. 2 (August 8, 2014): 234–81. http://dx.doi.org/10.1007/s10955-014-1087-7.

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Дисертації з теми "Comparison measures"

1

Jivsäter, Andreas, and Marcus Martinsson. "A comparison of management performance measures." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-21644.

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2

Salim, Naomie. "Analysis and comparison of molecular similarity measures." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274970.

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3

Mezo, Peter Geza. "A Comparison of Four Measures of Self-Control Skills." Thesis, University of Hawaii at Manoa, 2002. http://hdl.handle.net/10125/7083.

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4

Bletzinger, Kara. "A Comparison of Two Measures of Oral Reading Fluency." TopSCHOLAR®, 2006. http://digitalcommons.wku.edu/theses/441.

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Reading fluency is an important part of the process of learning to read. It is commonly assessed by the use of Curriculum-Based Measurement (CBM) techniques; however, a new assessment method emerged in the Woodcock-Johnson Tests of Achievement- Third Edition (WJ-III). Only one previous study had examined the validity of the Reading Fluency test from the WJ-III by comparing it to established CBM measures of reading fluency for a sample of third-grade students. The resulting correlation between the two measures supported the validity of the WJIII Reading Fluency test, but it was unclear as to the two tests' relationship across grade levels. To address this question, the current study examined the relationship between the Dynamic Indicators of Basic Early Literacy Skills test of Oral Reading Fluency (DORF) a standardized CBM-type measure, and the WJ-III Reading Fluency test with students from grades one, three, and five. Results supported the use of the WJ-III Reading Fluency test as a valid measure of reading fluency across grade levels.
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5

Wilde, Laura E. "A comparison of seven automated measures of syntactic complexity /." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2942.pdf.

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6

Wilde, Laura Elizabeth. "A Comparison of Seven Automated Measures of Syntactic Complexity." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1722.

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This study compared seven syntactic measures which can be automatically generated by the Computerized Profiling (CP) software: Mean Length of Utterance in morphemes or words (MLUm or MLUw), Mean Syntactic Length (MSL), the Index of Productive Syntax (IPSyn), the Picture Elicited Scoring Procedure (PESP) for the Language Analysis Remediation and Screening Profile (LARSP), the Syntactic Complexity Score (MSC) scoring of LARSP, and Developmental Sentence Scoring (DSS). Language samples came from 192 children, 106 typically developing children, ages 5;6 to 11;2 and 86 children with language impairment, ages 5;6 to 11;1. Patterns of correlation were consistent for children with or without language impairment. All measures were computed with CP software, and all coding decisions that were made by the software were accepted. The three measures of length (MLUm, MLUw, and MSL) were highly intercorrelated. MSC correlated with the measures of length and with DSS. DSS correlated with the length measures, though not as highly as MSC. DSS also correlated with IPSyn. IPSyn correlated moderately with PESP, correlated less with MSC, and correlated the least with the measures of length. PESP correlated moderately with each measure. PESP, DSS, and IPSyn correlated more highly for the children with language impairment. These measures correlated highly sometimes and sometimes they did not correlate much. This suggests that they are measuring different aspects of syntactic ability.
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Kowalchuk, Rhonda K. D. "Repeated measures multiple comparison procedures with a mixed model analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ51642.pdf.

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8

Fisher, Michael John. "A comparison of two depression measures for people with schizophrenia." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ64951.pdf.

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9

Rousseau, Glenna S. Vernon Laura. "A comparison of personal attribute and scenario based shame measures." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Fall/Thesis/ROUSSEAU_GLENNA_56.pdf.

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10

Lee, Daryl Hsu Ann. "Toward large-graph comparison measures to understand Internet topology dynamics." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37658.

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Approved for public release; distribution is unlimited
By measuring network changes, we can get a better understanding of a network. Extending this to the Internet, we are able to understand the constantly occuring changes on an international scale. In this research, we propose a measure that conveys the relative magnitude of the change between two networks (i.e., Internet topology). The measure is normalised and intuitively gives an indication of whether the change is small or large. We start off by applying this measure to standard common graphs, as well as random graphs. These graphs were first simulated and the measurements taken; results were then proved theoretically. These corresponded to the simulation results, thus demonstrating correctness. For case studies, we compared actual implemented networks with that which is inferred by probes. This comparison was done to study how accurate the probes were in discovering actual network topology. Finally, we conducted real-world experiments by applying the measurements to certain segments of the Internet. We observed that the measurements indeed do pick up events which significantly influenced structural changes to the Internet.
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Книги з теми "Comparison measures"

1

Madden, David. A comparison of poverty and welfare measures. Dublin: University College Dublin, Department of Economics, 1997.

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2

Dechema, ed. Safety standards and regulations for chemical plant [i.e. plants] in Europe: A comparison. Frankfurt am Main: Dechema, 1988.

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3

Pinsent, Philip John. A comparison of tests of recall as measures of foreign language listening comprehension. [Honolulu]: Department of English as a Second Language, University o f Hawaii at Manoa, 1986.

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4

Donovan, Deborah. Comparison of mobile source emission control measures to reduce ozone in Eastern Massachusetts. Cambridge, Mass: John F. Kennedy School of Government, 1992.

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5

Korea (South). Sikpʻum Ŭiyakpʻum Anjŏnchʻŏng., ред. Ŭiryoyong pangsanŭng pigyo chʻŭkchŏng =: Comparison of radioactivity in nuclear medicine. [Seoul]: Sikpʻum Ŭiyakpʻum Anjŏnchʻŏng Ŭiryo Kigi Pʻyŏngkabu Pangsasŏn Pʻyojuntʻim, 2007.

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6

Karahan, Saltuk Bugra. Comparison of performance measures in the virtual environment and real world land navigation tasks. Monterey, Calif: Naval Postgraduate School, 2000.

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7

Roelen, A. L. C. Causal risk models of air transport: Comparison of user needs and model capabilities. Amsterdam: IOS Press, 2008.

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8

Roelen, A. L. C. Causal risk models of air transport: Comparison of user needs and model capabilities. Amsterdam: IOS Press, 2008.

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9

Roelen, A. L. C. Causal risk models of air transport: Comparison of user needs and model capabilities. Amsterdam: IOS Press, 2008.

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10

Roelen, A. L. C. Causal risk models of air transport: Comparison of user needs and model capabilities. Amsterdam: IOS Press, 2008.

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Частини книг з теми "Comparison measures"

1

Ahmed, Mahmuda, Sophia Karagiorgou, Dieter Pfoser, and Carola Wenk. "Quality Measures for Map Comparison." In Map Construction Algorithms, 71–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25166-0_6.

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2

Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Other Measures for Model Comparison." In Springer Actuarial, 175–228. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57556-4_6.

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3

Saastamoinen, Kalle. "Logical Comparison Measures in Classification of Data—Nonmetric Measures." In Time Series Analysis and Forecasting, 173–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96944-2_12.

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4

Carmichael, Gordon A. "Comparison: Standardization and Decomposition." In Fundamentals of Demographic Analysis: Concepts, Measures and Methods, 49–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23255-3_2.

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5

Breckling, Jens. "Comparison of Different Measures of Association." In The Analysis of Directional Time Series: Applications to Wind Speed and Direction, 169–82. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3688-7_8.

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6

Forero, Manuel G., Carlos Arias-Rubio, and Brigete Tatiana González. "Analytical Comparison of Histogram Distance Measures." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 81–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_10.

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7

Stemmer, Georg, Stefan Steidl, Elmar Nöth, Heinrich Niemann, and Anton Batliner. "Comparison and Combination of Confidence Measures." In Text, Speech and Dialogue, 181–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46154-x_25.

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8

Van der Weken, Dietrich, Mike Nachtegael, and Etienne Kerre. "Using Similarity Measures for Histogram Comparison." In Lecture Notes in Computer Science, 396–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44967-1_47.

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9

González, Javier, and Alberto Muñoz. "Spectral Measures for Kernel Matrices Comparison." In Lecture Notes in Computer Science, 727–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_74.

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10

Ganzeboom, Harry B. G., and Donald J. Treiman. "Three Internationally Standardised Measures for Comparative Research on Occupational Status." In Advances in Cross-National Comparison, 159–93. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9186-7_9.

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Тези доповідей конференцій з теми "Comparison measures"

1

Lopez-Molina, C., C. Marco-Detchart, H. Bustince, J. Fernandez, A. Lopez-Maestresalas, and D. Ayala-Martini. "Hyperspectrum comparison using similarity measures." In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). IEEE, 2017. http://dx.doi.org/10.1109/ifsa-scis.2017.8023259.

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2

Vinh, Nguyen Xuan, Julien Epps, and James Bailey. "Information theoretic measures for clusterings comparison." In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553511.

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3

Drotar, Peter, and Zdenek Smekal. "Comparison of stability measures for feature selection." In 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2015. http://dx.doi.org/10.1109/sami.2015.7061849.

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4

Kua, Jia Min Karen, Julien Epps, Mohaddeseh Nosratighods, Eliathamby Ambikairajah, and Eric Choi. "Using clustering comparison measures for speaker recognition." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947592.

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5

Dai, Qi, Lihua Li, Tuan Pham, Tuan Pham, and Xiaobo Zhou. "Novel Probabilistic Measures for DNA Sequence Comparison." In 2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS-09). AIP, 2010. http://dx.doi.org/10.1063/1.3314269.

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6

Halvarsson, Bjorn. "Comparison of some Gramian based interaction measures." In 2008 IEEE International Conference on Computer-Aided Control Systems (CACSD) part of the Multi-Conference on Systems and Control. IEEE, 2008. http://dx.doi.org/10.1109/cacsd.2008.4627362.

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7

Koo, Imhoi, Xiang Zhang, and Seongho Kim. "Comparison of Spectral Similarity Measures for Compound Identification." In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2011. http://dx.doi.org/10.1109/icbbe.2011.5780011.

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8

Fliegel, Karel, Jana Kolmasova, and Jan Svihlik. "Performance comparison of perceived image color difference measures." In Applications of Digital Image Processing XLI, edited by Andrew G. Tescher. SPIE, 2018. http://dx.doi.org/10.1117/12.2321644.

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Rosen, Gail. "Comparison of Autoregressive Measures for DNA Sequence Similarity." In 2007 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2007. http://dx.doi.org/10.1109/gensips.2007.4365814.

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Yalazan, Hakki Tarkan. "Comparison of Three Objective Image Quality Measures' Performances." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298607.

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Звіти організацій з теми "Comparison measures"

1

Edney, Carey. A comparison of three measures of waking fantasy. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2988.

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2

Gust, Jeff C., and Georgia L. Harris. Weights and measures division quality manual for proficiency testing and interlaboratory comparison. Gaithersburg, MD: National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.ir.7214.

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3

Zablotsky, Benjamin, Emily Terlizzi, Jennifer Madans, Stephen Blumberg, and Julie Weeks. Assessing Anxiety and Depression: A Comparison of National Health Interview Survey Measures. National Center for Health Statistics (U.S.), July 2022. http://dx.doi.org/10.15620/cdc:117491.

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4

Kircher, John C., Ted Packard, Brian G. Bell, and Paul C. Bernhard. Comparison of Skin Conductance and Skin Resistance Measures for the Detection of Deception. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada404127.

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5

Dukart, Carla. A Comparison of Speech Intelligibility Measures between Unsophisticated Listener Judgements and Orthographic Transcription. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7130.

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6

Schulz, David S. A Comparison of Alternative Measures of the Qualified Military Available and Interested Recruit Market. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada267435.

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7

Barrett, Garry, Peter Levell, and Kevin Milligan. A Comparison of Micro and Macro Expenditure Measures Across Countries Using Differing Survey Methods. Cambridge, MA: National Bureau of Economic Research, October 2013. http://dx.doi.org/10.3386/w19544.

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8

Conklin, Edward. A comparison of measures of emotions from written reports of dreams and waking experiences. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5251.

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9

Young, Sabrina K., Elina T. Page, Abigail Mary Okrent, and Megan Sweitzer. Assessment and adjustment of body weight measures in scanner data. Washington, D.C.: Economic Research Service, U.S. Department of Agriculture, June 2023. http://dx.doi.org/10.32747/2023.8054018.ers.

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USDA, Economic Research Service compares use of self-reported height and weight data available in scanner data with measured height and weight data to calculate body mass index (BMI) of household members and identifies methods for adjusting self-reported data to improve accuracy. The report also includes a comparison of methods for defining the obesity status of a household.
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10

Baader, Franz, Oliver Fernández Gil, and Pavlos Marantidis. Approximation in Description Logics: How Weighted Tree Automata Can Help to Define the Required Concept Comparison Measures in FL₀. Technische Universität Dresden, 2016. http://dx.doi.org/10.25368/2022.230.

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Recently introduced approaches for relaxed query answering, approximately defining concepts, and approximately solving unification problems in Description Logics have in common that they are based on the use of concept comparison measures together with a threshold construction. In this paper, we will briefly review these approaches, and then show how weighted automata working on infinite trees can be used to construct computable concept comparison measures for FL₀ that are equivalence invariant w.r.t. general TBoxes. This is a first step towards employing such measures in the mentioned approximation approaches.
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Також вас можуть зацікавити розширені добірки на тему "Comparison measures" для конкретних типів джерел:
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Ми пропонуємо знижки на всі преміум-плани для авторів, чиї праці увійшли до тематичних добірок літератури. Зв'яжіться з нами, щоб отримати унікальний промокод!

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