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Journal articles on the topic 'Select'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 May 2024

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1

Bhattacharjee, Y. "Panel Selects Most Dangerous Select Agents." Science 332, no. 6037 (June 23, 2011): 1491–92. http://dx.doi.org/10.1126/science.332.6037.1491.

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2

Hull, D. L. "Introduction." Selection 3, no. 1 (November 2002): 5–6. http://dx.doi.org/10.1556/select.3.2002.1.1.

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Bickerton, D. "Book Review: Bart de Boer:The Origins of Vowel Systems. 2001, Oxford University Press, xiii + 168 pp. (ISBN 0 19 829965 6)." Selection 3, no. 1 (November 2002): 127–30. http://dx.doi.org/10.1556/select.3.2002.1.10.

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4

de Jong, T. J., and S. A. H. Geritz. "Erratum." Selection 3, no. 1 (November 2002): 131. http://dx.doi.org/10.1556/select.3.2002.1.11.

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5

Radick, G. "Darwin on Language and Selection." Selection 3, no. 1 (November 2002): 7–16. http://dx.doi.org/10.1556/select.3.2002.1.2.

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Hull, D. L. "Species, Languages and the Comparative Method." Selection 3, no. 1 (November 2002): 17–28. http://dx.doi.org/10.1556/select.3.2002.1.3.

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7

Delisle, R. G. "Evolutionary Biology and Linguistics: The Nature and Implication of a Disciplinary Integration." Selection 3, no. 1 (November 2002): 29–44. http://dx.doi.org/10.1556/select.3.2002.1.4.

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8

Mufwene, Salikoko S. "Competition and Selection in Language Evolution." Selection 3, no. 1 (November 2002): 45–56. http://dx.doi.org/10.1556/select.3.2002.1.5.

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9

Wilkins, John S. "Darwinian Metaphor and Analogy: The Things That Evolve in Life and Language." Selection 3, no. 1 (November 2002): 57–74. http://dx.doi.org/10.1556/select.3.2002.1.6.

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Croft, William. "The Darwinization of Linguistics." Selection 3, no. 1 (November 2002): 75–91. http://dx.doi.org/10.1556/select.3.2002.1.7.

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11

Knudsen, T. "The Significance of Tacit Knowledge in the Evolution of Human Language." Selection 3, no. 1 (November 2002): 93–112. http://dx.doi.org/10.1556/select.3.2002.1.8.

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Whitehead, H., P. J. Richerson, and R. Boyd. "Cultural Selection and Genetic Diversity in Humans." Selection 3, no. 1 (November 2002): 115–25. http://dx.doi.org/10.1556/select.3.2002.1.9.

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13

&NA;. "Select Facts about SELECT." Oncology Times 23, no. 9 (September 2001): 1. http://dx.doi.org/10.1097/01.cot.0000293088.44183.76.

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14

Kohl, David F. "To Select or Not Select." Collection Management 26, no. 2 (October 2001): 1–12. http://dx.doi.org/10.1300/j105v26n02_01.

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15

Fischer, Martin, and Bernd Balle. "Reliably Measure at the Push of a Button – The new, intelligent ZEISS O-SELECT digital measuring projector." Advanced Technologies in Mechanics 2, no. 1(2) (July 7, 2015): 23. http://dx.doi.org/10.17814/atim.2015.1(2).15.

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ZEISS O-SELECT allows you to quickly and reproducibly measure at the push of a button. The digital measuring projector autonomously selects the right distance, automatically eliminates blur at the edges and even chooses the right measurement program. O-SELECT independently recognizes features such as circles or lines and suggests characteristics such as distance and radius.

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16

Lewis, Richard A. "Select." Journal of the American Dental Association 122, no. 9 (September 1991): 12. http://dx.doi.org/10.14219/jada.archive.1991.0279.

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17

. "Select." Psychopraxis 6, no. 3 (June 2004): 110–14. http://dx.doi.org/10.1007/bf03072155.

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18

. "Select." Psychopraxis 6, no. 5 (October 2004): 190–95. http://dx.doi.org/10.1007/bf03072179.

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19

. "Select." Psychopraxis 6, no. 6 (December 2004): 225–27. http://dx.doi.org/10.1007/bf03072187.

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20

. "Select." Psychopraxis 7, no. 4 (August 2005): 141–45. http://dx.doi.org/10.1007/bf03072219.

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21

. "Select." Psychopraxis 8, no. 2 (April 2006): 82–87. http://dx.doi.org/10.1007/bf03072269.

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22

. "Select." Psychopraxis 8, no. 4 (August 2006): 162–67. http://dx.doi.org/10.1007/bf03072293.

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23

. "Select." Psychopraxis, jaargang 2007 9, no. 3 (June 2007): 122–27. http://dx.doi.org/10.1007/bf03072354.

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. "Select." Psychopraxis, jaargang 2007 9, no. 5 (October 2007): 203–8. http://dx.doi.org/10.1007/bf03072380.

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. "Select." Psychopraxis, jaargang 2007 9, no. 6 (December 2007): 248–53. http://dx.doi.org/10.1007/bf03072390.

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26

Tooley, James. "TO SELECT OR NOT TO SELECT?" Economic Affairs 16, no. 4 (September 1996): 35. http://dx.doi.org/10.1111/j.1468-0270.1996.tb00556.x.

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27

Li, Haoxuan, Xueyan Zhang, Ziyan Li, and Chunyuan Zheng. "Overview of Machine Learning for Stock Selection Based on Multi-Factor Models." E3S Web of Conferences 214 (2020): 02047. http://dx.doi.org/10.1051/e3sconf/202021402047.

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In recent years, many scholars have used different methods to predict and select stocks. Empirical studies have shown that in multi-factor models, machine learning algorithms perform better on stock selection than traditional statistical methods. This article selects six classic machine learning algorithms, and takes the CSI 500 component stocks as an example, using 19 factors to select stocks. In this article, we introduce four of these algorithms in detail and apply them to select stocks. Finally, we back-test six machine learning algorithms, list the data, analyze the performance of each algorithm, and put forward some ideas on the direction of machine learning algorithm improvement.

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28

Liu, Lu, Junheng Gao, Georgia Beasley, and Sin-Ho Jung. "LASSO and Elastic Net Tend to Over-Select Features." Mathematics 11, no. 17 (August 30, 2023): 3738. http://dx.doi.org/10.3390/math11173738.

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Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end. The LASSO approach selects features with large regression estimates, rather than based on statistical significance, that are associated with the outcome by imposing an L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select even more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model may lose prediction accuracy. In this paper, we propose to use standard regression methods, without any penalizing approach, combined with a stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation-based method selects a very small number of features while maintaining a high prediction power, whereas LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high-dimensional data, even without advanced bioinformatics knowledge.

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29

Best, Terence, and Handel. "Select Handel." Musical Times 127, no. 1718 (May 1986): 282. http://dx.doi.org/10.2307/965470.

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30

Feldmann, Louise Mort. "Faculty Select." Charleston Advisor 24, no. 1 (July 1, 2022): 19–23. http://dx.doi.org/10.5260/chara.24.1.19.

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EBSCO's Faculty Select database provides a central platform to discover open educational resources (OERs) and digital rights management (DRM)-free e-books. OER content is from several established and well-known content providers: BC Open Textbook, Milne Open Textbook, OpenStax, and the Open Textbook Library. The database also searches OAPEN Library's Open Access content and DRM-free e-books available for purchase from EBSCO. The database is configurable, upon implementation, to search EBSCO's Open Access Monograph Collection and owned DRM-free e-books. Users can preview content and then request course-friendly links for OER and owned DRM-free e-books or request the purchase of DRM-free e-books. This database may be of interest to faculty and librarians searching for affordable course content to replace or supplement traditional textbooks, depending on the institution's budget and needs.

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31

Traynor, Desmond, Clem Cairns, and Colm Tóibín. "Select Lists." Books Ireland, no. 227 (1999): 370. http://dx.doi.org/10.2307/20631967.

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32

Capelotti, P. J. "Select References." Septentrio Conference Series, no. 3 (September 9, 2015): 97. http://dx.doi.org/10.7557/5.3587.

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33

Mathäs, Alexander. "Select Bibliography." Konturen 6 (November 18, 2014): 17. http://dx.doi.org/10.5399/uo/konturen.7.0.3539.

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34

Kalygin, Viktor. "Select Bibliography." Studia Celto-Slavica 1 (2006): 319–32. http://dx.doi.org/10.54586/fzax5207.

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35

Drewry, Gavin. "Select committees." Contemporary Record 3, no. 2 (November 1989): 19–20. http://dx.doi.org/10.1080/13619468908581054.

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36

Drewry, Gavin. "Select committees." Contemporary Record 4, no. 4 (April 1991): 29–30. http://dx.doi.org/10.1080/13619469108581151.

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37

Henriksen, Ingrid, Jaakko Autio, Sverre A. Christensen, and Claus Wohlert. "Select bibliography." Scandinavian Economic History Review 46, no. 3 (September 1998): 75–86. http://dx.doi.org/10.1080/03585522.1998.10414750.

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38

Lee, Jo Ann. "Please Select MePlease Select Me." PsycCRITIQUES 5151, no. 2727 (2006). http://dx.doi.org/10.1037/a0003072.

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39

KLEIN, ERIC A., IAN M. THOMPSON, SCOTT M. LIPPMAN, PHYLLIS J. GOODMAN, DEMETRIUS ALBANES, PHILIP R. TAYLOR, and CHARLES COLTMAN. "SELECT:." Journal of Urology, October 2001, 1311–15. http://dx.doi.org/10.1097/00005392-200110000-00020.

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40

"Select." Psychopraxis 7, no. 2 (April 2005): 67–72. http://dx.doi.org/10.1007/bf03072204.

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41

"Select Bibliography." Cahiers Élisabéthains 81, no. 1 (January 1, 2012): 147–48. http://dx.doi.org/10.7227/ce.spiss12.22.

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42

"PAIS select." Choice Reviews Online 35, no. 03 (November 1, 1997): 35–1292. http://dx.doi.org/10.5860/choice.35-1292.

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43

"SELECT BIBLIOGRAPHY." Parliamentary History 41, S1 (October 2022): 501–20. http://dx.doi.org/10.1111/1750-0206.12658.

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44

"Select Bibliography." Chinese Studies in History 27, no. 3-4 (April 1994): 257–64. http://dx.doi.org/10.2753/csh0009-4633270304257.

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45

"Select Bibliography." Chinese Studies in Philosophy 23, no. 3-4 (April 1992): 258–71. http://dx.doi.org/10.2753/csp1097-1467230304258.

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46

"Faculty Select." AYS Open, July 19, 2020. http://dx.doi.org/10.21428/da2f8f94.48fcc331.

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47

"SELECT B." Alloy Digest 34, no. 10 (October 1, 1985). http://dx.doi.org/10.31399/asm.ad.ts0448.

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Abstract SELECT B is a fully uniform air-hardening die steel having abrasion resistance intermediate between Olympic (AISI Type D2) high-carbon, high-chromium die steel and Badger (AISI Type O1) oil-hardening die steel. SELECT B has excellent non-deforming, deep-hardening characteristics and good toughness. Its machinability is comparable to most oil-hardening tool steels. Among its many uses are cold blanking and forming dies, rolls, punches, gages and broaches. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on forming, heat treating, and machining. Filing Code: TS-448. Producer or source: Latrobe Steel Company.

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48

"Select Bibliography." International Journal of the History of Sport 18, no. 3 (September 2001): 197–202. http://dx.doi.org/10.1080/714001591.

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49

"Select Bibliography." International Journal of the History of Sport 19, no. 2-3 (July 2002): 408–16. http://dx.doi.org/10.1080/714001755.

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50

"Select Bibliography." Soccer & Society 2, no. 2 (June 2001): 171–73. http://dx.doi.org/10.1080/714004841.

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