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Auswahl der wissenschaftlichen Literatur zum Thema „Individual differences scaling“
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Zeitschriftenartikel zum Thema "Individual differences scaling"
Malkoc, G., P. Kay und M. A. Webster. „Individual differences in hue scaling“. Journal of Vision 3, Nr. 12 (28.03.2010): 34. http://dx.doi.org/10.1167/3.12.34.
Der volle Inhalt der QuelleEmery, Kara, David Peterzell, Vicki Volbrecht und Michael Webster. „Factors underlying individual differences in hue scaling“. Journal of Vision 16, Nr. 12 (01.09.2016): 1148. http://dx.doi.org/10.1167/16.12.1148.
Der volle Inhalt der QuelleRuette, T., und D. Speelman. „Transparent aggregation of variables with Individual Differences Scaling“. Literary and Linguistic Computing 29, Nr. 1 (23.02.2013): 89–106. http://dx.doi.org/10.1093/llc/fqt011.
Der volle Inhalt der QuelleKreiman, Jody, Bruce R. Gerratt, Kristin Precoda und Gerald S. Berke. „Individual Differences in Voice Quality Perception“. Journal of Speech, Language, and Hearing Research 35, Nr. 3 (Juni 1992): 512–20. http://dx.doi.org/10.1044/jshr.3503.512.
Der volle Inhalt der QuelleAdams, L., N. Chronos, R. Lane und A. Guz. „The measurement of breathlessness induced in normal subjects: individual differences“. Clinical Science 70, Nr. 2 (01.02.1986): 131–40. http://dx.doi.org/10.1042/cs0700131.
Der volle Inhalt der QuelleKöhn, Hans-Friedrich. „Combinatorial individual differences scaling within the city-block metric“. Computational Statistics & Data Analysis 51, Nr. 2 (November 2006): 931–46. http://dx.doi.org/10.1016/j.csda.2005.09.013.
Der volle Inhalt der QuelleShrivastav, Rahul. „Multidimensional Scaling of Breathy Voice Quality: Individual Differences in Perception“. Journal of Voice 20, Nr. 2 (Juni 2006): 211–22. http://dx.doi.org/10.1016/j.jvoice.2005.04.005.
Der volle Inhalt der QuelleWinter, Edward M. „Scaling: Partitioning out Differences in Size“. Pediatric Exercise Science 4, Nr. 4 (November 1992): 296–301. http://dx.doi.org/10.1123/pes.4.4.296.
Der volle Inhalt der QuelleNowicki, Julie R., und Bruce G. Coury. „Individual Differences in Processing Strategy for a Bargraph Display“. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, Nr. 19 (Oktober 1993): 1315–19. http://dx.doi.org/10.1518/107118193784162317.
Der volle Inhalt der QuelleKim, Kyung-Sun, Eun-Young Yoo, Nahyun Kwon und Sei-Ching Joanna Sin. „Individual differences in source selection behavior: Profile analyses via multidimensional scaling“. Proceedings of the American Society for Information Science and Technology 46, Nr. 1 (2009): 1–5. http://dx.doi.org/10.1002/meet.2009.14504603119.
Der volle Inhalt der QuelleDissertationen zum Thema "Individual differences scaling"
Kapnoula, Efthymia Evangelia. „Individual differences in speech perception: sources, functions, and consequences of phoneme categorization gradiency“. Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/3115.
Der volle Inhalt der QuelleFrank, Erika. „A STATISTICAL APPROACH FOR IDENTIFICATION OF CHEMICAL GROUPINGS OF ELEMENTS IN SWEDISH ROCKS WITH SPECIAL FOCUS ON ARSENIC AND SULPHUR“. Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447527.
Der volle Inhalt der QuelleSousa, Samuel Anderson Alves de 1983. „Novas metodologias para a análise de dados em ciências ômicas e para o controle de qualidade de amostras de biodiesel-diesel“. [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/248548.
Der volle Inhalt der QuelleTese (doutorado) - Universidade Estadual de Campinas, Instituto de Química
Made available in DSpace on 2018-08-25T12:46:59Z (GMT). No. of bitstreams: 1 Sousa_SamuelAndersonAlvesde_D.pdf: 6563141 bytes, checksum: df96f3f360351f7d74d92a5834369ecf (MD5) Previous issue date: 2013
Resumo: Neste trabalho são apresentadas duas novas metodologias multivariadas. Na primeira, é desenvolvida uma ferramenta denominada bucketing otimizado para a correção dos desalinhamentos dos espectros de RMN 1H. A análise de componentes principais em intervalos (iPCA) é utilizada para explorar espectros de RMN 1H e 13C. Para a diminuição de ruído destes últimos é utilizada a análise de componentes principais em múltiplas escalas (MSPCA). Os modelos iPCA são construídos para as classes de amostras, metropolitanas e não metropolitanas, em conjunto e separadas, atuando complementarmente na detecção de amostras não conformes. Neste contexto, os padrões espectrais apontaram amostras, previamente reprovadas pelos parâmetros físico-químicos próprios do campo de biocombustíveis. Adicionalmente, os modelos reprovaram amostras com padrões espectrais distintos, não reprovadas pelos parâmetros citados. De modo geral, o desempenho dos modelos utilizando os espectros de RMN 1H foi satisfatório. Uma exceção foi a detecção de amostras fora da especificação para o teor de biodiesel, onde as distinções nos espectros não permitiram a discriminação de amostras com teores próximo ao limite. Contudo, ao se estender um pouco a faixa sugerida na legislação, os modelos mostraram boa melhoria. Os modelos a partir dos espectros de RMN 13C obtiveram desempenho inferior àqueles citados acima. No segundo estudo é apresentado um novo método denominado escalamento de diferenças individuais multinível (ML-INDSCAL), para analisar a variação intra-individual em dados das ciências ômicas, focando em mudanças nas covariâncias dentro dos grupos experimentais e evidenciando as relações entre as variáveis (BVRs). Como somente a variação intra-individual é usada para revelar as BVRs associadas às mudanças dinâmicas, as interpretações sobre o fenômeno no qual os efeitos se baseiam são melhoradas. Um conjunto de dados simulado é explorado para demonstrar a força do método. O método é também aplicado a um conjunto real de dados de um estudo de expressões genéticas em células expressando a proteína viral R (Vpr) na forma nativa e com as mutações R80A e F72A/R73A. O procedimento jack-knife é explorado na validação dos modelos ML-INDSCAL. O método ML-INDSCAL é o primeiro da literatura que combina a exploração da estrutura multinível do conjunto de dados e a investigação de BVRs e pode fornecer valiosas contribuições no campo de seleção de características
Abstract: In this work, two new multivariate methodologies are presented. In the first approach, a tool named optimized bucketing is developed to correct 1H NMR spectra misalignments. The interval principal component analysis (iPCA) is used in order to explore 1H and 13C NMR spectra. The multiscale principal component analysis (MSPCA) is used for denoising of 13C NMR spectra. The iPCA models are built for two classes of samples, metropolitan and non-metropolitan, together and isolated, complementarily providing out-of-specification samples detections. In this context, the spectral profiles pointed out samples out of specification, in accordance to their previously known physical-chemical parameters from the field of biofuels. Additionally, the models were able to identify samples with distinct spectral profiles, but not rejected by the cited parameters. In general, the iPCA models using 1H NMR spectra presented good performances. An exception involves the detection of out-of-specification samples for biodiesel content, where the distinction on spectra profiles did not allow discrimination of samples when the biodiesel content was close to the allowed limit. Nevertheless, a small extension in the range, adopted by the Brazilian legislation, was enough to produce an improvement. The models from the 13C NMR spectra achieved worse performance than those cited above. In the second study is presented a novel method named multilevel individual differences scaling (ML-INDSCAL) to analyze within-individual variation in omic data, focusing on the changing covariances within groups and evidencing the between variables relationships (BVRs). Since only the within-individual variation is used to reveal the BVRs associated to dynamic changes, the interpretations about the real phenomena underlying the treatment are improved. A simulated data set is explored to demonstrate the strength of the method. Also, the method is applied to a real data set from a study of expression profiles in cell lines expressing wild-type and two mutated (R80A and F72A/R73A strains) Vpr. A version of the jack-knife procedure is explored in order to validate the ML-INDSCAL models. The ML-INDSCAL is the first method in literature that combines the exploration of the multilevel structure and the BVRs investigation and it can provide valuable insights on the feature selection field
Doutorado
Físico-Química
Doutor em Ciências
Dwyer, Theodore James. „An Assessment of Paired Similarities and Card Sorting“. [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000158.
Der volle Inhalt der QuelleBuchteile zum Thema "Individual differences scaling"
Cieciuch, Jan. „Multidimensional Scaling (MDS)“. In Encyclopedia of Personality and Individual Differences, 3015–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1329.
Der volle Inhalt der QuelleCieciuch, Jan. „Multidimensional Scaling (MDS)“. In Encyclopedia of Personality and Individual Differences, 1–4. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-28099-8_1329-1.
Der volle Inhalt der QuelleDing, Cody S. „Individual Differences MDS Model“. In Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research, 97–108. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78172-3_7.
Der volle Inhalt der QuelleOksanen, Jari, und Pertti Huttunen. „Finding a common ordination for several data sets by individual differences scaling“. In Progress in theoretical vegetation science, 137–45. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1934-1_11.
Der volle Inhalt der QuelleLiu, Daniel. „Scaling from Weather to Climate“. In Cultural Inquiry, 93–117. Berlin: ICI Berlin Press, 2020. http://dx.doi.org/10.37050/ci-17_05.
Der volle Inhalt der QuelleDunn-Rankin, Peter, Gerald A. Knezek, Susan Wallace und Shuqiang Zhang. „Individual Differences Scaling“. In Scaling Methods, 185–97. 2. Aufl. Psychology Press, 2014. http://dx.doi.org/10.1201/9781410611048-19.
Der volle Inhalt der Quelle„Individual differences models“. In Multidimensional Scaling, Second Edition. Chapman and Hall/CRC, 2000. http://dx.doi.org/10.1201/9781420036121.ch10.
Der volle Inhalt der Quelle„Multidimensional Scaling of Individual Differences: Individual Distortions of a Mean Structure“. In The Quantitative Analysis of Social Representations, 97–114. Routledge, 2014. http://dx.doi.org/10.4324/9781315040998-14.
Der volle Inhalt der QuelleTeremko, Vasyl. „PRINT AS THE CONTEXTUAL FACTOR OF MODERN AGE“. In Integration of traditional and innovation processes of development of modern science. Publishing House “Baltija Publishing”, 2020. http://dx.doi.org/10.30525/978-9934-26-021-6-10.
Der volle Inhalt der QuelleOsorio, Roberto, und Lisa Borland. „Distributions of High-Frequency Stock-Market Observables“. In Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.003.0023.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Individual differences scaling"
Zou, Shangyin, Yuki Ban und Shin'ichi Warisawa. „Investigating Individual Differences in Olfactory Adaptation to Pulse Ejection Odor Display by Scaling Olfaction Sensitivity of Intensity“. In 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2021. http://dx.doi.org/10.1109/vrw52623.2021.00127.
Der volle Inhalt der QuelleMasui, Hideki, Makoto Kashiwagi, Wolfgang Mu¨ller und Bertrand Lante`s. „Suggestion to Waste Classification for Scaling Factor Method“. In ASME 2003 9th International Conference on Radioactive Waste Management and Environmental Remediation. ASMEDC, 2003. http://dx.doi.org/10.1115/icem2003-5007.
Der volle Inhalt der QuelleLi, Y. G., M. F. Abdul Ghafir, L. Wang, R. Singh, K. Huang und X. Feng. „Non-Linear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm“. In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22285.
Der volle Inhalt der QuelleGeekiyanage, Suranga C. H., Adrian Ambrus und Dan Sui. „Feature Selection for Kick Detection With Machine Learning Using Laboratory Data“. In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95496.
Der volle Inhalt der QuelleCasey, Ross D., Michael J. Brandemuehl, Tim Merrigan und Jay Burch. „Performance Modeling of an Air-Based Photovoltaic/Thermal (PV/T) Collector“. In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90474.
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