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Journal articles on the topic 'Bootstrappin'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Riloff, Ellen, and Rosie Jones. "A Retrospective on Mutual Bootstrapping." AI Magazine 39, no. 1 (March 27, 2018): 51–61. http://dx.doi.org/10.1609/aimag.v39i1.2778.

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When we were invited to write a retrospective article about our AAAI-99 paper on mutual bootstrapping (Riloff and Jones 1999), our first reaction was hesitation because, well, that algorithm seems old and clunky now. But upon reflection, it shaped a great deal of subsequent work on bootstrapped learning for natural language processing, both by ourselves and others. So our second reaction was enthusiasm, for the opportunity to think about the path from 1999 to 2017 and to share the lessons that we learned about bootstrapped learning along the way. This article begins with a brief history of related research that preceded and inspired the mutual bootstrapping work, to position it with respect to that period of time. We then describe the general ideas and approach behind the mutual bootstrapping algorithm. Next, we overview several types of research that have followed and shared similar themes: multi-view learning, bootstrapped lexicon induction, and bootstrapped pattern learning. Finally, we discuss some of the general lessons that we have learned about bootstrapping techniques for NLP to offer guidance to researchers and practitioners who may be interested in exploring these types of techniques in their own work.

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2

Méndez-Suárez, Mariano. "Marketing Mix Modeling Using PLS-SEM, Bootstrapping the Model Coefficients." Mathematics 9, no. 15 (August 3, 2021): 1832. http://dx.doi.org/10.3390/math9151832.

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Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis.

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3

Jong-Woo Kim and Bai-Sun Kong. "Low-Voltage Bootstrapped CMOS Drivers With Efficient Conditional Bootstrapping." IEEE Transactions on Circuits and Systems II: Express Briefs 55, no. 6 (June 2008): 556–60. http://dx.doi.org/10.1109/tcsii.2007.916843.

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4

Gutman, Ariel, Isabelle Dautriche, Benoît Crabbé, and Anne Christophe. "Bootstrapping the Syntactic Bootstrapper: Probabilistic Labeling of Prosodic Phrases." Language Acquisition 22, no. 3 (December 15, 2014): 285–309. http://dx.doi.org/10.1080/10489223.2014.971956.

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5

Gemel, Aleksander. "Kwestia pojęciowej nieciągłości procesu nabywania dokładnych reprezentacji numerycznych w teorii bootstrappingu." Humanistyka i Przyrodoznawstwo, no. 22 (August 14, 2018): 101–18. http://dx.doi.org/10.31648/hip.373.

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Artykuł poświęcony jest analizie kwestii nieciągłości pojęciowej w zaproponowanej przez Susan Carey teorii nabywania reprezentacji liczby dokładnej, tzw. bootstrapping theory. Pierwsza część tekstu stanowi przegląd głównych stanowisk arytmetyki kognitywnej, druga przedstawia kluczowe założenia procesu bootstrappingu, zaś część poświęcona jest krytycznej analizie pojęcia nieciągłości w zaproponowanej przez Carey teorii uczenia. Zgodnie z centralną tezą, proces bootstrappingu nie daje się ująć jako procedura kształtowania systemu reprezentacji, który byłby nieciągły względem wrodzonych systemów wiedzy rdzennej.

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6

Huang, Francis L. "Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters." Educational and Psychological Measurement 78, no. 2 (November 24, 2016): 297–318. http://dx.doi.org/10.1177/0013164416678980.

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Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

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7

Steels, Luc, and Frédéric Kaplan. "AIBO’s first words." Evolution of Communication 4, no. 1 (December 31, 2001): 3–32. http://dx.doi.org/10.1075/eoc.4.1.03ste.

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This paper explores the hypothesis that language communication in its very first stage is bootstrapped in a social learning process under the strong influence of culture. A concrete framework for social learning has been developed based on the notion of a language game. Autonomous robots have been programmed to behave according to this framework. We show experiments that demonstrate why there has to be a causal role of language on category acquisition; partly by showing that it leads effectively to the bootstrapping of communication and partly by showing that other forms of learning do not generate categories usable in communication or make information assumptions which cannot be satisfied.

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8

Gunter, Bert. "Bootstrapping." Infection Control and Hospital Epidemiology 15, no. 8 (August 1994): 543–47. http://dx.doi.org/10.2307/30148407.

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9

Wagner, L. "Bootstrapping." Trends in Cognitive Sciences 2, no. 11 (November 1998): 427. http://dx.doi.org/10.1016/s1364-6613(98)01254-6.

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10

Kahlon, Vineet. "Bootstrapping." ACM SIGPLAN Notices 43, no. 6 (May 30, 2008): 249–59. http://dx.doi.org/10.1145/1379022.1375613.

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11

Gunter, Bert. "Bootstrapping." Infection Control and Hospital Epidemiology 15, no. 8 (August 1994): 543–47. http://dx.doi.org/10.1086/646975.

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12

Dewi, Yumi Novita, and Findi Ayu Sariasih. "METODE SAMPLE BOOTSTRAPPING UNTUK MENINGKATKAN PERFORMA ALGORITMA NAIVE BAYES PADA CITRA TUNGGAL PAP SMEAR." JURNAL TEKNIK INFORMATIKA 12, no. 1 (June 20, 2019): 1–10. http://dx.doi.org/10.15408/jti.v12i1.11031.

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ABSTRACT Research on cell classification of single pap smear images is an interesting thing to discuss, where the value of consent is very important to determine whether the cells are normal or not. An example of this study is to determine whether using the bootstraping sample method can improve the performance of the Bayes naive algorithm to classify single pap smear images that are on the herlev dataset. Approval values will be given for two classes and seven classes. The method used consists of several stages, namely preprocessing, knowledge rules, evaluation, and performance reports. The results of this study prove that the bootstrap sample method can increase the accuracy of seven classes to 85.24% and 93.24% for accuracy values with two classes.Keywords: Sample Bootstrapping; Naive Bayes; Pap Smear. ABSTRAK Penelitian mengenai klasifikasi sel citra tunggal pap smear menjadi hal yang menarik untuk dibahas, dimana nilai akurasi tersebut sangat penting untuk menetukan apakah sel-sel tersebut normal atau tidak. Penelitian ini bertujuan untuk menentukan apakah penggunaan metode sample bootstrapping dapat meningkatkan kinerja algoritma naive bayes untuk mengklasifikasikan citra tunggal pap smear yang ada pada dataset herlev. Nilai akurasi akan diperiksa untuk dua kelas dan tujuh kelas. Metode yang digunakan terdiri dari beberapa tahapan yaitu preprocessing, knowledge rule, evaluation, dan performance report. Hasil penelitian ini menunjukkan bahwa metode sample bootstrapping dapat meningkatkan nilai akurasi tujuh kelas menjadi 85,24% dan 93,24% untuk nilai akurasi dengan dua kelas.

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13

von Hobe, Cord-Friedrich, Marius Michels, and Oliver Musshoff. "Technical efficiency and productivity change in German large-scale arable farming." German Journal of Agricultural Economics 70, no. 1 (March 1, 2021): 36–48. http://dx.doi.org/10.30430/70.2021.1.36-48.

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This paper provides an assessment of technical efficiency and productivity change for a sample of large-scale arable farms in Germany. For this, the paper applies input-oriented Data Envelopment Analysis (DEA) and Malmquist Index (MI) methods in combination with bootstrapping to a balanced five-year panel data set of 86 German large-scale arable farms over a time period from 2012/2013 to 2016/2017. The DEA results of the original sample show a mean input-saving potential of 9.2 % across farms and time periods. The bootstrapped confidence intervals indicate no statistically significant difference among the mean scores for individual years, however significant differences exist between individual farms. The results of the MI analysis of the original sample suggest a mean annual growth in total factor productivity of 5.4 %. This progress was driven by technical change (6.5 %) and happened despite a small average deterioration in change in technical efficiency (1.1 %). The progress in total factor productivity as well as technical change is statistically underpinned through the bootstrapped confidence intervals. The result of change in technical efficiency computed from the original sample cannot be confirmed statistically as the corresponding confidence interval includes unity.

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14

Le Teuff, G., A. Venot, E. Walter, J. Coste, and C. Quantin. "Improving Model Robustness with Bootstrapping." Methods of Information in Medicine 44, no. 05 (2005): 704–11. http://dx.doi.org/10.1055/s-0038-1634028.

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Summary Objective: Recent results published by Coste et al. in discriminant analysis with ordinal responses showed the superiority of optimal discriminating analysis for ordinal responses (ODAO) both in terms of classification and simplicity of implementation compared to classic methods (Fisher’s discrimination, logistic regression) applied to medical data (prognostics of burns) and to simulated data. Nevertheless, the solutions obtained by ODAO may be sensitive to re-sampling (i.e the estimated coefficients by ODAO may show excessive sensitivity to the training sample). This study proposes some solutions to control the fluctuations of sampling and to ensure model stability. Methods: We used intensive computational methods and bootstrapping, at the outset of model building in order to reduce the sampling variability of estimated coefficients. Thus, the estimation of the coefficients was not based on the minimization of a classification criterion of the training sample, but on the minimization of an aggregate criterion of bootstrapped replications of a classification criterion. Five aggregate criteria were studied. Results: The improvement in terms of robustness appeared in 30% of the test cases with moderate training sample size and 55% of those with small training sample size. Conclusion: Simulated test cases showed that bootstrapping can help construct more robust models in difficult classification situations and small training samples which are particularly frequent.

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15

Drozdek, Adam. "Bootstrapping Faith." Philosophical Inquiry 20, no. 3 (1998): 31–40. http://dx.doi.org/10.5840/philinquiry1998203/413.

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16

Vogel, Jonathan. "Epistemic Bootstrapping." Journal of Philosophy 105, no. 9 (2008): 518–39. http://dx.doi.org/10.5840/jphil2008105931.

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17

Appel, Andrew W. "Axiomatic bootstrapping." ACM Transactions on Programming Languages and Systems 16, no. 6 (November 1994): 1699–718. http://dx.doi.org/10.1145/197320.197336.

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18

Hacquard, Valentine. "Bootstrapping attitudes." Semantics and Linguistic Theory 24 (April 5, 2015): 330. http://dx.doi.org/10.3765/salt.v24i0.2434.

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This paper explores two classic problems at the semantics-pragmatics interface from a learner’s perspective. First, the meaning that speakers convey often goes beyond the literal meaning of the sentences they utter. Second, not all content encoded in utterances has equal standing: some is foregrounded, some backgrounded. Yet a sentence does not formally distinguish what a speaker asserts from what she presupposes or merely implicates. For this reason, the child acquiring a language has a daunting task. She must both extract the literal meaning from the overall message, and separate the background assumptions that are linguistically required from those that are incidental. This paper discusses the ways in which the syntax might guide the child with this daunting task, through a few case studies on children’s acquisition of attitude verbs.

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19

Fisher, Cynthia, Yael Gertner, Rose M. Scott, and Sylvia Yuan. "Syntactic bootstrapping." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 2 (February 24, 2010): 143–49. http://dx.doi.org/10.1002/wcs.17.

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20

Gawlitzek-Maiwald, Ira, and Rosemarie Tracy. "Bilingual bootstrapping." First Language 14, no. 42-43 (October 1994): 324–25. http://dx.doi.org/10.1177/014272379401404229.

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21

Christy, David S. "Bootstrapping Trade." World Policy Journal 25, no. 4 (2008): 127–31. http://dx.doi.org/10.1162/wopj.2009.25.4.127.

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22

Carpenter, James M. "UNINFORMATIVE BOOTSTRAPPING." Cladistics 12, no. 2 (June 1996): 177–81. http://dx.doi.org/10.1111/j.1096-0031.1996.tb00201.x.

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23

Darling, Stephen, Richard J. Allen, and Jelena Havelka. "Visuospatial Bootstrapping." Current Directions in Psychological Science 26, no. 1 (February 2017): 3–9. http://dx.doi.org/10.1177/0963721416665342.

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Visuospatial bootstrapping is the name given to a phenomenon whereby performance on visually presented verbal serial-recall tasks is better when stimuli are presented in a spatial array rather than a single location. However, the display used has to be a familiar one. This phenomenon implies communication between cognitive systems involved in storing short-term memory for verbal and visual information, alongside connections to and from knowledge held in long-term memory. Bootstrapping is a robust, replicable phenomenon that should be incorporated in theories of working memory and its interaction with long-term memory. This article provides an overview of bootstrapping, contextualizes it within research on links between long-term knowledge and short-term memory, and addresses how it can help inform current working memory theory.

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24

Douven, Igor, and Christoph Kelp. "Proper bootstrapping." Synthese 190, no. 1 (May 4, 2012): 171–85. http://dx.doi.org/10.1007/s11229-012-0115-x.

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25

Li, Zhouping, and Liang Peng. "Bootstrapping endpoint." Sankhya A 74, no. 1 (February 2012): 126–40. http://dx.doi.org/10.1007/s13171-012-0015-7.

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26

Singh, Sarjinder, and Stephen A. Sedory. "Sufficient bootstrapping." Computational Statistics & Data Analysis 55, no. 4 (April 2011): 1629–37. http://dx.doi.org/10.1016/j.csda.2010.10.010.

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27

White, Graham. "Bootstrapping Normativity." Philosophy & Technology 24, no. 1 (December 30, 2010): 35–53. http://dx.doi.org/10.1007/s13347-010-0005-4.

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28

Metcalf, Patricia A., Michelle L. Meyer, Chirayath M. Suchindran, and Gerardo Heiss. "Assessment of a Regression Method to Reclassify Deaths Attributable to Heart Failure." Global Journal of Health Science 9, no. 3 (July 12, 2016): 13. http://dx.doi.org/10.5539/gjhs.v9n3p13.

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<p><strong>BACKGROUND: </strong>Evaluation of cause-specific mortality for public health research depends on accurate death certificates and vital records. However, ill-defined causes of death (termed garbage codes), such as heart failure, are often listed as the underlying cause of death. We examined a regression method proposed by Ahern and colleagues for redistributing deaths attributed to heart failure and compared it to a simulation of the regression method by bootstrapping. </p><p><strong>METHODS: </strong>Deaths attributed to heart failure in four U.S. states (Maryland, Minnesota, Mississippi and North Carolina) were redistributed to a set of underlying causes of death using regression models that identified the proportion of deaths for each target code within a given state-age-sex-education group using ICD-10 mortality data. The results were compared with 3,000 bootstrapped samples with replacement regression.</p><p><strong>RESULTS: </strong>The odds of death from heart failure in the population studied increased with age, was higher in whites and lower in decedents with greater than a high school education compared to those with less than high school education. There were 18 (29.0%) subgroups that showed no significant redistribution targets for the Ahern regression method and 28 (45.2%) for the bootstrapped regression method. Ischemic heart disease was a distribution target for 28 (45.2%) of the Ahern regression subgroups and 22 (35.5%) of the bootstrapped regression subgroups. The Ahern regression method and bootstrapped regression methods were discordant in 19 (30.6%) out of the 62 subgroups examined.</p><p><strong>CONCLUSION: </strong>The Ahern regression method tended to redistribute deaths attributed to heart failure to more target groups compared with the bootstrapped regression method. Both the Ahern regression and the bootstrap regression methods were computationally intensive and inefficient, and results appeared to be influenced by the choices of sex-age-education group strata. Other methods such as coarsened exact matching and improvements to the Ahern approach are desirable additions to the tools available to mitigate the impact of garbage codes on the accuracy of death certification.</p>

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29

Choi, Hyungwook, and Joongmoo Byun. "Automatic Velocity Analysis by using an High-resolution Bootstrapped Differential Semblance Method." Geophysics and Geophysical Exploration 16, no. 4 (November 30, 2013): 225–33. http://dx.doi.org/10.7582/gge.2013.16.4.225.

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30

Jentsch, Carsten, and Christian H. Weiß. "Bootstrapping INAR models." Bernoulli 25, no. 3 (August 2019): 2359–408. http://dx.doi.org/10.3150/18-bej1057.

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31

Perry, D. A., M. P. Amaranthus, J. G. Borchers, S. L. Borchers, and R. E. Brainerd. "Bootstrapping in Ecosystems." BioScience 39, no. 4 (April 1989): 230–37. http://dx.doi.org/10.2307/1311159.

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32

Weisberg, Jonathan. "The Bootstrapping Problem." Philosophy Compass 7, no. 9 (August 22, 2012): 597–610. http://dx.doi.org/10.1111/j.1747-9991.2012.00504.x.

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33

Kiverstein, Julian, and Andy Clark. "Bootstrapping the mind." Behavioral and Brain Sciences 31, no. 1 (February 2008): 41–58. http://dx.doi.org/10.1017/s0140525x07003330.

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AbstractAfter offering a brief account of how we understand the shared circuits model (SCM), we divide our response into four sections. First, in section R1, we assess to what extent SCM is committed to an account of the ontogeny and phylogeny of shared circuits. In section R2, we examine doubts raised by several commentators as to whether SCM might be expanded so as to accommodate the mirroring of emotions, sensations, and intransitive actions more generally. Section R3 responds to various criticisms that relate to the account of social-learning Hurley proposes in the target article. We conclude in section R4 by responding to a number of commentators who argued for the limitation of control theory as a framework for studying social cognition.

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34

Solow, Andrew R. "Bootstrapping correlated data." Journal of the International Association for Mathematical Geology 17, no. 7 (October 1985): 769–75. http://dx.doi.org/10.1007/bf01031616.

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35

Cuculiere, Roger, and Albert Stadler. "Successful Bootstrapping: 10715." American Mathematical Monthly 107, no. 5 (May 2000): 465. http://dx.doi.org/10.2307/2695314.

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36

Lu, Huchuan, Xiaoning Zhang, Jinqing Qi, Na Tong, Xiang Ruan, and Ming-Hsuan Yang. "Co-Bootstrapping Saliency." IEEE Transactions on Image Processing 26, no. 1 (January 2017): 414–25. http://dx.doi.org/10.1109/tip.2016.2627804.

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37

Apolloni, Bruno, Simone Bassis, Sabrina Gaito, and Dario Malchiodi. "Bootstrapping complex functions." Nonlinear Analysis: Hybrid Systems 2, no. 2 (June 2008): 648–64. http://dx.doi.org/10.1016/j.nahs.2006.12.003.

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38

Jiménez Gamero, M. D., J. Muñoz García, and A. Muñoz Reyes. "Bootstrapping statistical functionals." Statistics & Probability Letters 39, no. 3 (August 1998): 229–36. http://dx.doi.org/10.1016/s0167-7152(98)00055-8.

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39

Zarepour, M. "Bootstrapping convex hulls." Statistics & Probability Letters 45, no. 1 (October 1999): 55–63. http://dx.doi.org/10.1016/s0167-7152(99)00042-5.

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40

Li, Hongyi, and G. S. Maddala. "Bootstrapping cointegrating regressions." Journal of Econometrics 80, no. 2 (October 1997): 297–318. http://dx.doi.org/10.1016/s0304-4076(97)00043-2.

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41

Aerts, M., P. Janssen, and N. Veraverbeke. "Bootstrapping regression quantiles." Journal of Nonparametric Statistics 4, no. 1 (January 1994): 1–20. http://dx.doi.org/10.1080/10485259408832597.

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42

Heylighen, Francis. "Bootstrapping knowledge representations." Kybernetes 30, no. 5/6 (July 2001): 691–725. http://dx.doi.org/10.1108/eum0000000005695.

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43

Chatterjee, A., and S. N. Lahiri. "Bootstrapping Lasso Estimators." Journal of the American Statistical Association 106, no. 494 (June 2011): 608–25. http://dx.doi.org/10.1198/jasa.2011.tm10159.

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44

Kim, Dae-hak, and Hyeong-Chul Jeong. "Bootstrapping Logit Model." Communications for Statistical Applications and Methods 9, no. 1 (April 1, 2002): 281–89. http://dx.doi.org/10.5351/ckss.2002.9.1.281.

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45

Carpenter, J. "FORUM: UNINFORMATIVE BOOTSTRAPPING." Cladistics 12, no. 2 (June 1996): 177–81. http://dx.doi.org/10.1006/clad.1996.0013.

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46

Romano, Joseph P. "Bootstrapping the mode." Annals of the Institute of Statistical Mathematics 40, no. 3 (September 1988): 565–86. http://dx.doi.org/10.1007/bf00053066.

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47

Joun, M. "Bootstrapping density estimates." Communications in Statistics - Theory and Methods 17, no. 1 (January 1988): 61–78. http://dx.doi.org/10.1080/03610928808829610.

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48

Csorgo, Sandor, and David M. Mason. "Bootstrapping Empirical Functions." Annals of Statistics 17, no. 4 (December 1989): 1447–71. http://dx.doi.org/10.1214/aos/1176347374.

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49

Berkowitz, Jeremy, and Francis X. Diebold. "Bootstrapping Multivariate Spectra." Review of Economics and Statistics 80, no. 4 (November 1998): 664–66. http://dx.doi.org/10.1162/003465398557753.

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50

Franke, Jürgen, and Michael H. Neumann. "Bootstrapping Neural Networks." Neural Computation 12, no. 8 (August 1, 2000): 1929–49. http://dx.doi.org/10.1162/089976600300015204.

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Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called bootstrap, which is based on an imitation of the probabilistic structure of the data-generating process on the basis of the information provided by a given set of random observations. In this article we investigate this classical method in the context of artificial neural networks used for estimating a mapping from input to output space. We establish consistency results for bootstrap estimates of the distribution of parameter estimates.

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