Relevant bibliographies by topics / Statistical learning theory

Academic literature on the topic 'Statistical learning theory'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Statistical learning theory"

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Wu, Yuhai. "Statistical Learning Theory." Technometrics 41, no. 4 (November 1999): 377–78. http://dx.doi.org/10.1080/00401706.1999.10485951.

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Vapnik, Vladimir, and Rauf Izmailov. "Rethinking statistical learning theory: learning using statistical invariants." Machine Learning 108, no. 3 (July 18, 2018): 381–423. http://dx.doi.org/10.1007/s10994-018-5742-0.

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Vapnik, V. N. "Complete Statistical Theory of Learning." Automation and Remote Control 80, no. 11 (November 2019): 1949–75. http://dx.doi.org/10.1134/s000511791911002x.

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Shi, Luyuan. "Statistical Learning in Game Theory." Journal of Applied Mathematics and Physics 11, no. 03 (2023): 663–69. http://dx.doi.org/10.4236/jamp.2023.113043.

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Kulkarni, Sanjeev R., and Gilbert Harman. "Statistical learning theory: a tutorial." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 6 (June 10, 2011): 543–56. http://dx.doi.org/10.1002/wics.179.

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Cherkassky, V. "The Nature Of Statistical Learning Theory~." IEEE Transactions on Neural Networks 8, no. 6 (November 1997): 1564. http://dx.doi.org/10.1109/tnn.1997.641482.

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Wechsler, H., Z. Duric, F. Li, and V. Cherkassky. "Motion estimation using statistical learning theory." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 4 (April 2004): 466–78. http://dx.doi.org/10.1109/tpami.2004.1265862.

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Estes, William K. "Toward a statistical theory of learning." Psychological Review 101, no. 2 (1994): 282–89. http://dx.doi.org/10.1037/0033-295x.101.2.282.

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Lippi, Marco. "Statistical Relational Learning for Game Theory." IEEE Transactions on Computational Intelligence and AI in Games 8, no. 4 (December 2016): 412–25. http://dx.doi.org/10.1109/tciaig.2015.2490279.

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Vapnik, V. N. "An overview of statistical learning theory." IEEE Transactions on Neural Networks 10, no. 5 (1999): 988–99. http://dx.doi.org/10.1109/72.788640.

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Dissertations / Theses on the topic "Statistical learning theory"

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Liang, Annie. "Economic Theory and Statistical Learning." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493561.

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This dissertation presents three independent essays in microeconomic theory. Chapter 1 suggests an alternative to the common prior assumption, in which agents form beliefs by learning from data, possibly interpreting the data in different ways. In the limit as agents observe increasing quantities of data, the model returns strict solutions of a limiting complete information game, but predictions may diverge substantially for small quantities of data. Chapter 2 (with Jon Kleinberg and Sendhil Mullainathan) proposes use of machine learning algorithms to construct benchmarks for “achievable" predictive accuracy. The paper illustrates this approach for the problem of predicting human-generated random sequences. We find that leading models explain approximately 10-15% of predictable variation in the problem. Chapter 3 considers the problem of how to interpret inconsistent choice data, when the observed departures from the standard model (perfect maximization of a single preference) may emerge either from context-dependencies in preference or from stochastic choice error. I show that if preferences are “simple" in the sense that they consist only of a small number of context-dependencies, then the analyst can use a proposed optimization problem to recover the true number of underlying context-dependent preferences.
Economics
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Deng, Xinwei. "Contributions to statistical learning and statistical quantification in nanomaterials." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29777.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Wu, C. F. Jeff; Committee Co-Chair: Yuan, Ming; Committee Member: Huo, Xiaoming; Committee Member: Vengazhiyil, Roshan Joseph; Committee Member: Wang, Zhonglin. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Hill, S. "Applications of statistical learning theory to signal processing problems." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604048.

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The dissertation focuses on the applicability of Support Vector Regression (SVR) in signal processing contexts. This is shown to be particularly well-suited to filtering in alpha-stable noise environments, and a further slight modification is proposed to this end. The main work in this dissertation on SVR is on the application to audio filtering based on perceptual criteria. This appears an ideal solution to the problem due to the fact that the loss function typically used by perceptual audio filtering practitioners incorporates a region of zero loss, as does SVR. SVR is extended to the problem of complex-valued regression, for application in the audio filtering problem to the frequency domain. This is with regions of zero loss that are both square and circular, and the circular case is extended to the problem of vector-valued regression. Three experiments are detailed with a mix of both good and poor results, and further refinements are proposed. Polychotomous, or multi-category classification is then studied. Many previous attempts are reviewed, and compared. A new approach is proposed, based on a geometrical structure. This is shown to overcome many of the problems identified with previous methods in addition to being very flexible and efficient in its implementation. This architecture is also derived, for just the three-class case, using a complex-valued kernel function. The general architecture is used experimentally in three separate implementations to give a demonstration of the overall approach. The methodology is shown to achieve results comparable to those of many other methods, and to include many of them as special cases. Further possible refinements are proposed which should drastically reduce optimisation times for so-called 'all-together' methods.
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Hu, Qiao Ph D. Massachusetts Institute of Technology. "Application of statistical learning theory to plankton image analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/39206.

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Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2006.
Includes bibliographical references (leaves 155-173).
A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed.
(cont.) One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not "good" ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large real-world dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.
by Qiao Hu.
Ph.D.
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Shipitsyn, Aleksey. "Statistical Learning with Imbalanced Data." Thesis, Linköpings universitet, Filosofiska fakulteten, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139168.

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In this thesis several sampling methods for Statistical Learning with imbalanced data have been implemented and evaluated with a new metric, imbalanced accuracy. Several modifications and new algorithms have been proposed for intelligent sampling: Border links, Clean Border Undersampling, One-Sided Undersampling Modified, DBSCAN Undersampling, Class Adjusted Jittering, Hierarchical Cluster Based Oversampling, DBSCAN Oversampling, Fitted Distribution Oversampling, Random Linear Combinations Oversampling, Center Repulsion Oversampling. A set of requirements on a satisfactory performance metric for imbalanced learning have been formulated and a new metric for evaluating classification performance has been developed accordingly. The new metric is based on a combination of the worst class accuracy and geometric mean. In the testing framework nonparametric Friedman's test and post hoc Nemenyi’s test have been used to assess the performance of classifiers, sampling algorithms, combinations of classifiers and sampling algorithms on several data sets. A new approach of detecting algorithms with dominating and dominated performance has been proposed with a new way of visualizing the results in a network. From experiments on simulated and several real data sets we conclude that: i) different classifiers are not equally sensitive to sampling algorithms, ii) sampling algorithms have different performance within specific classifiers, iii) oversampling algorithms perform better than undersampling algorithms, iv) Random Oversampling and Random Undersampling outperform many well-known sampling algorithms, v) our proposed algorithms Hierarchical Cluster Based Oversampling, DBSCAN Oversampling with FDO, and Class Adjusted Jittering perform much better than other algorithms, vi) a few good combinations of a classifier and sampling algorithm may boost classification performance, while a few bad combinations may spoil the performance, but the majority of combinations are not significantly different in performance.
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Wang, Hongyan. "Analysis of statistical learning algorithms in data dependent function spaces /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ma-b23750534f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.
"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [87]-100)
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Gianvecchio, Steven. "Application of information theory and statistical learning to anomaly detection." W&M ScholarWorks, 2010. https://scholarworks.wm.edu/etd/1539623563.

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In today's highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection.
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Srivastava, Santosh. "Bayesian minimum expected risk estimation of distributions for statistical learning /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6765.

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Wang, Ni. "Statistical Learning in Logistics and Manufacturing Systems." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11457.

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This thesis focuses on the developing of statistical methodology in reliability and quality engineering, and to assist the decision-makings at enterprise level, process level, and product level. In Chapter II, we propose a multi-level statistical modeling strategy to characterize data from spatial logistics systems. The model can support business decisions at different levels. The information available from higher hierarchies is incorporated into the multi-level model as constraint functions for lower hierarchies. The key contributions include proposing the top-down multi-level spatial models which improve the estimation accuracy at lower levels; applying the spatial smoothing techniques to solve facility location problems in logistics. In Chapter III, we propose methods for modeling system service reliability in a supply chain, which may be disrupted by uncertain contingent events. This chapter applies an approximation technique for developing first-cut reliability analysis models. The approximation relies on multi-level spatial models to characterize patterns of store locations and demands. The key contributions in this chapter are to bring statistical spatial modeling techniques to approximate store location and demand data, and to build system reliability models entertaining various scenarios of DC location designs and DC capacity constraints. Chapter IV investigates the power law process, which has proved to be a useful tool in characterizing the failure process of repairable systems. This chapter presents a procedure for detecting and estimating a mixture of conforming and nonconforming systems. The key contributions in this chapter are to investigate the property of parameter estimation in mixture repair processes, and to propose an effective way to screen out nonconforming products. The key contributions in Chapter V are to propose a new method to analyze heavily censored accelerated life testing data, and to study the asymptotic properties. This approach flexibly and rigorously incorporates distribution assumptions and regression structures into estimating equations in a nonparametric estimation framework. Derivations of asymptotic properties of the proposed method provide an opportunity to compare its estimation quality to commonly used parametric MLE methods in the situation of mis-specified regression models.
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Rydén, Otto. "Statistical learning procedures for analysis of residential property price indexes." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207946.

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Residential Price Property Indexes (RPPIs) are used to study the price development of residential property over time. Modeling and analysing an RPPI is not straightforward due to residential property being a heterogeneous good. This thesis focuses on analysing the properties of the two most conventional hedonic index modeling approaches, the hedonic time dummy method and the hedonic imputation method. These two methods are analysed with statistical learning procedures from a regression perspective, specifically, ordinary least squares regression, and a number of more advanced regression approaches, Huber regression, lasso regression, ridge regression and principal component regression. The analysis is based on the data from 56 000 apartment transactions in Stockholm during the period 2013-2016 and results in several models of a RPPI. These suggested models are then validated using both qualitative and quantitative methods, specifically a bootstrap re-sampling to perform analyses of an empirical confidence interval for the index values and a mean squared errors analysis of the different index periods. Main results of this thesis show that the hedonic time dummy index methodology produces indexes with smaller variances and more robust indexes for smaller datasets. It is further shown that modeling of RPPIs with robust regression generally results in a more stable index that is less affected by outliers in the underlying transaction data. This type of robust regression strategy is therefore recommended for a commercial implementation of an RPPI.
Bostadsprisindex används för att undersöka prisutvecklingen för bostäder över tid. Att modellera ett bostadsprisindex är inte alltid lätt då bostäder är en heterogen vara. Denna uppsats analyserar skillnaden mellan de tvåhuvudsakliga hedoniska indexmodelleringsmetoderna, som är, hedoniska tiddummyvariabelmetoden och den hedoniska imputeringsmetoden. Dessa metoder analyseras med en statistisk inlärningsprocedur gjord utifrån ett regressionsperspektiv, som inkluderar analys utav minsta kvadrats-regression, Huberregression, lassoregression, ridgeregression och principal componentregression. Denna analys är baserad på ca 56 000 lägenhetstransaktioner för lägenheter i Stockholm under perioden 2013-2016 och används för att modellera era versioner av ett bostadsprisindex. De modellerade bostadsprisindexen analyseras sedan med hjälp utav både kvalitativa och kvantitativa metoder inklusive en version av bootstrap för att räkna ut ett empiriskt konfidensintervall för bostadsprisindexen samt en medelfelsanalys av indexpunktskattningarna i varje tidsperiod. Denna analys visar att den hedoniska tid-dummyvariabelmetoden producerar bostadsprisindex med mindre varians och ger också robustare bostadsprisindex för en mindre datamängd. Denna uppsats visar också att användandet av robustare regressionsmetoder leder till stabilare bostadsprisindex som är mindre påverkade av extremvärden, därför rekommenderas robusta regressionsmetoder för en kommersiell implementering av ett bostadsprisindex.
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Books on the topic "Statistical learning theory"

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Vapnik, Vladimir Naumovich. Statistical learning theory. New York: Wiley, 1998.

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Emmert-Streib, Frank, and Matthias Dehmer, eds. Information Theory and Statistical Learning. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-84816-7.

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Emmert-Streib, Frank. Information Theory and Statistical Learning. Boston, MA: Springer US, 2009.

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Vapnik, Vladimir N. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-3264-1.

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Catoni, Olivier. Statistical Learning Theory and Stochastic Optimization. Edited by Jean Picard. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b99352.

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Vapnik, Vladimir N. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2440-0.

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Vapnik, Vladimir Naumovich. The nature of statistical learning theory. New York: Springer, 1995.

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Watanabe, Sumio. Algebraic geometry and statistical learning theory. Cambridge: Cambridge University Press, 2009.

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Vapnik, Vladimir Naumovich. The Nature of Statistical Learning Theory. New York, NY: Springer New York, 1995.

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Kulkarni, Sanjeev, and Gilbert Harman. An Elementary Introduction to Statistical Learning Theory. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118023471.

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Book chapters on the topic "Statistical learning theory"

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Fernandes de Mello, Rodrigo, and Moacir Antonelli Ponti. "Statistical Learning Theory." In Machine Learning, 75–128. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5_2.

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Chowdhary, K. R. "Statistical Learning Theory." In Fundamentals of Artificial Intelligence, 415–43. New Delhi: Springer India, 2020. http://dx.doi.org/10.1007/978-81-322-3972-7_14.

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Tempo, Roberto, Giuseppe Calafiore, and Fabrizio Dabbene. "Statistical Learning Theory." In Randomized Algorithms for Analysis and Control of Uncertain Systems, 123–34. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4610-0_9.

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Zwald, Laurent, Olivier Bousquet, and Gilles Blanchard. "Statistical Properties of Kernel Principal Component Analysis." In Learning Theory, 594–608. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_41.

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Golden, Richard M. "Set Theory for Concept Modeling." In Statistical Machine Learning, 65–81. First edition. j Boca Raton, FL : CRC Press, 2020. j Includes bibliographical references and index.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351051507-2.

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Altun, Yasemin, and Alex Smola. "Unifying Divergence Minimization and Statistical Inference Via Convex Duality." In Learning Theory, 139–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_13.

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Hoyle, David C., and Magnus Rattray. "A Statistical Mechanics Analysis of Gram Matrix Eigenvalue Spectra." In Learning Theory, 579–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27819-1_40.

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Harman, Gilbert, and Sanjeev Kulkarni. "Statistical Learning Theory and Induction." In Encyclopedia of the Sciences of Learning, 3186–88. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_692.

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Weston, Jason. "Statistical Learning Theory in Practice." In Empirical Inference, 81–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6_9.

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Bousquet, Olivier, Stéphane Boucheron, and Gábor Lugosi. "Introduction to Statistical Learning Theory." In Advanced Lectures on Machine Learning, 169–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28650-9_8.

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Conference papers on the topic "Statistical learning theory"

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Samuelsson, Christer. "Linguistic theory in statistical language learning." In the Joint Conferences. Morristown, NJ, USA: Association for Computational Linguistics, 1998. http://dx.doi.org/10.3115/1603899.1603915.

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Tian, Jing, Ming-hu Ha, Jun-hua Li, and Da-zeng Tian. "The Fuzzy- Number Based Key Theorem of Statistical Learning Theory." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258536.

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Yun-Chao Bai and Ming-Hu Ha. "The key theorem of statistical learning theory on possibility spaces." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527708.

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Nasien, Dewi, Siti S. Yuhaniz, and Habibollah Haron. "Statistical Learning Theory and Support Vector Machines." In 2010 Second International Conference on Computer Research and Development. IEEE, 2010. http://dx.doi.org/10.1109/iccrd.2010.183.

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Gelly, Sylvain, Olivier Teytaud, Nicolas Bredeche, and Marc Schoenauer. "A statistical learning theory approach of bloat." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068309.

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"Session MA4b: Information theory and statistical learning." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869050.

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Yang, Liu, Hu Shicheng, Dong Kaikun, Li Bin, and Xu Yongdong. "The Key Theorem of Statistical Learning Theory with Fuzzy Samples." In 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.214.

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Liu, Yang, Kai-kun Dong, Li Guo, and Xing-ling Yuan. "The Key Theorem of Statistical Learning Theory with Rough Samples." In 2009 WRI World Congress on Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.23.

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Gibbs, Alison L., and Alex Stringer. "The Fundamental Role of Computation in Teaching Statistical Theory." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.rmcxl.

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What skills, knowledge and habits of mind does a statistician require in order to contribute effectively as an inhabitant of the data science ecosystem? We describe a new course in statistical theory that was developed as part of our consideration of this question. The course is a core requirement in a new curriculum for undergraduate students enrolled in statistics programs of study. Problem solving and critical thinking are developed through both mathematical and computational thinking and all ideas are motivated through questions to be answered from large, open and messy data. Central to the development of the course is the tenet that the use of computation is as fundamental to statistical thinking as the use of mathematics. We describe the course, including its connection to the learning outcomes of our new statistics program of study, and the multiple ways we use and integrate computation.
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Grubbs, Paul, Marie-Sarah Lacharite, Brice Minaud, and Kenneth G. Paterson. "Learning to Reconstruct: Statistical Learning Theory and Encrypted Database Attacks." In 2019 IEEE Symposium on Security and Privacy (SP). IEEE, 2019. http://dx.doi.org/10.1109/sp.2019.00030.

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Reports on the topic "Statistical learning theory"

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Moody, John. Statistical Learning Theory and Algorithms. Fort Belvoir, VA: Defense Technical Information Center, February 1993. http://dx.doi.org/10.21236/ada270209.

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Ilyin, M. E. The distance learning course «Theory of probability, mathematical statistics and random functions». OFERNIO, December 2018. http://dx.doi.org/10.12731/ofernio.2018.23529.

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Gruber, Peter. Teaching and Learning Statistics with ChatGPT. Instats Inc., 2023. http://dx.doi.org/10.61700/m71hf8ug0ces1469.

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This two-day workshop responds to the disruption that the AI revolution introduces to statistics education and practice. This seminar will equip academics and PhD students with the skills and insights needed to integrate ChatGPT into their statistics teaching and learning. Participants will gain hands-on experience with ChatGPT in classroom and assessment settings, as well as using it to prepare course material and exercises, while gaining a deeper understanding of how to use it to enhance statistics learning and practice. The seminar will also explore the wider institutional and ethical ramifications that the use of AI brings to education institutions and the wider economy. An official Instats certificate of completion and 2 ECTS Equivalent points are provided upon completion.
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Heckman, Stuart. Understanding insurance decisions: A review of risk management decision making, risk literacy, and racial/ethnic differences. Center for Insurance Policy and Research, January 2024. http://dx.doi.org/10.52227/26712.2024.

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The racial/ethnic wealth gap is a stunning feature of U.S. household finances. Although the causes of the gap are complex, it is important that researchers investigate disparities between racial/ethnic groups in household financial management areas. We posit that first understanding insurance decisions as a critical component of overall household financial management is an important avenue for further understanding factors that may perpetuate or reduce the racial wealth gap. Moreover, risk management, including the purchase and use of insurance products, is a key yet challenging area for household financial management. Therefore, this literature review focuses on research relevant to three main questions: 1) How do consumers make risk management decisions? 2) What key skills are required to make risk management decisions (with a focus on literacy and numeracy skills)? 3) Do these skills vary between racial/ethnic groups? Regarding the first question, we find that consumers are prone to errors when making decisions involving risk, but research shows that decisions can be improved. Skilled Decision Theory (SDT) highlights that cognitive ability plays less of a central role in decision-making and that decision-making is more of an acquired skill. Consequently, learning comprehension and confidence play a crucial role in the decision-making process. In terms of the second question and the skills needed to make appropriate risk management decisions, the literature suggests that insurance literacy, not necessarily financial literacy, as well as numeracy skills are likely to be critical prerequisites to good insurance choices. In particular, the importance of statistical numeracy in decision-making cannot be overstated. Finally for our third question, our review indicates that there is a relatively limited number of available studies focusing on racial/ethnic differences in risk management decisions and skills. While some studies find differences between racial/ethnic groups in various measures of financial literacy, the findings are overall mixed and, therefore, inconclusive. Researchers should verify if there are, in fact, differences or if the differences are due to other factors that vary by racial/ethnic category.
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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, July 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.
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Balyk, Nadiia, Yaroslav Vasylenko, Vasyl Oleksiuk, and Galina Shmyger. Designing of Virtual Cloud Labs for the Learning Cisco CyberSecurity Operations Course. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3177.

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The article is devoted to the study of the problem of the cybersecurity basics teaching. The training of the ICT-specialties students using the course “CCNA Cyber Operations” of the network academy Cisco is considered. At present, many universities have similar academies, while others can open them. On the basis of free software platforms Apache CloudStack and EVE-NG Community authors designed and implemented a virtual cloud laboratory. It operates according to the “IaaS” model. Thanks to the technology of embedded virtualization, the work of many virtual machines, storing of their status, traffic analysis and visualization of network topologies are maintained. The article describes the experience of teaching students of the specialty “Pedagogical education. ICT” in the course “CCNA Cyber Operations” with the use of virtual cloud laboratories. The authors have been conducted a survey of students who studied at the course. Its purpose was to determine how much they satisfied were with the course. Statistical processing of the results was performed on the basis of the Rasch model using the software MiniSteps.
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McGee, Steven, Randi McGee-Tekula, and Jennifer Duck. Does a Focus on Modeling and Explanation of Molecular Interactions Impact Student Learning and Identity? The Learning Partnership, April 2017. http://dx.doi.org/10.51420/conf.2017.1.

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The Interactions curriculum and professional development program is designed to support high school teachers in their transition to the physical science Next Generation Science Standards. Through curriculum materials, an online portal for delivering the digital materials, interactive models of molecular phenomena, and educative teacher guide, teachers are able to support students in bridging the gap between macroscopic and sub-microscopic ideas in physical science by focusing on a modeling and explanation-oriented exploration of attractions and energy changes at the atomic level. During the fall semester of the 2015-16 school year, The Learning Partnership conducted a field test of Interactions with eleven teachers who implemented the curriculum across a diverse set of school districts. As part of the field test, The Learning Partnership examined the impact of teachers’ inquiry-based teaching practices on student learning and identification with the scientific enterprise. The results indicate that students had statistically significant growth in learning from the beginning to end of unit 2 and that the extent to which teachers engaged students in inquiry had a positive statistically significant influence on the growth rate and a statistically significant indirect impact on students’ identification with the scientific enterprise.
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Crowe. PR-261-15609-R01 Machine Learning Algorithms for Smart Meter Diagnostics Part II (TR2701). Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010862.

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Modern smart meters provide an abundance of diagnostic data. Detecting abnormalities in this data can be difficult given the sheer quantity of information. Determining what kind of readings constitute normal operation versus an impending problem has been the subject of significant research; however, there is still room for improvement in real-time fault monitoring. Statistical models known as Machine Learning Algorithms (MLAs) have been identified as a potential solution. A new feature set was selected that allowed for extension of MLAs to ultrasonic meters with different path arrangements. Principal Component Analysis was used to give structure to and visualize multidimensional ultrasonic meter data. The results showed that MLAs may be extended to meters of different sizes, manufacturers, and from different flow facilities.
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Nkwenti, Michael N. Viable Learning Pathways Back into Schooling for Out-of-School Youths in Cameroon. Edited by Tony Mays. Commonwealth of Learning (COL), February 2023. http://dx.doi.org/10.56059/11599/5230.

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The share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment or training among the population of the corresponding age group: youth aged 15–24, people aged 15–29, or both age groups. The data show an increasing proportion of Cameroonian youth NEET. Incomplete schooling is likely to be one of the causes of their status. According to the Census and Economic Information Centre (CEIC) statistics, the share of youth NEET has been steadily increasing among female youth and fluctuating among male youth. There are about three times more female than male youth NEET. This report therefore explores the challenge of out-of-school children and youths in Cameroon. Various attempts have been made in the past to address the challenge but have not had significant impact on improving the situation. This report therefore proposes the establishment of a virtual open school — Cameroon National Open School (CAMNOS) — that can provide a virtual backbone for both online and blended provision, with the latter making use of existing day schools as after-hours support centres.
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Ahmed, Badrun Nessa, and Rizwana Islam. TEACHING AND LEARNING EXPERIENCE AT THE NATIONAL UNIVERSITY AFFILIATED TERTIARY COLLEGES IN BANGLADESH. Bangladesh Institute of Development Studies, March 2024. http://dx.doi.org/10.57138/axvn7639.

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The Government of Bangladesh is currently implementing the College Education Development Project (CEDP) to improve participating colleges' teaching and learning environment and strengthen the strategic planning and management capacity of National University (NU) affiliated tertiary colleges in Bangladesh. The focus of CEDP is to improve the capacity of the National University College system to plan, manage, implement, and monitor institutional programs, as well as strengthen the foundation for the next phase of development activities. CEDP promotes institution-led activities that focus on creating quality teaching-learning environments in government and non-government colleges through the availability of competitive grants. The achievement of the College Education Development Project (CEDP) is the satisfaction level of students, teachers, and employers in terms of the quality and relevance of teaching. To measure the satisfaction level of the relevant stakeholders (i.e., students, teachers, and employers), three beneficiary feedback surveys (i.e., baseline, mid-term, and endline) are planned to be conducted, among which the baseline was carried out in 2019. The Bangladesh Institute of Development Studies (BIDS) conducted the Mid-term Satisfaction Survey in May-June 2022. The mid-term survey is the second of the three planned surveys of the CEDP, measuring the mid-term satisfaction level of the stakeholders, students and teachers of National University-affiliated colleges, and employers of NU graduates. This study uses data from the Mid-term Satisfaction Survey to assess the mid-term satisfaction level of students, teachers, and employers. The study was designed using a mixed-method approach, both quantitative and qualitative, to address the objectives of this study. Data analysis has used both the baseline data collected in 2019 and the mid-term data collected in this study. Using the baseline and mid-term data, a two-round panel data was constructed at the college level. Depending on the specific indicators, the program's effect at the college level was calculated. We compare the overall satisfaction level regarding all the relevant indicators by stakeholder types, i.e., principals, teachers, and students, and observe differences among the average satisfaction levels. The overall teaching and learning environment satisfaction level is 3.81 among college principals, 2.95 among teachers, and 2.57 among students. A similar pattern is also found for other indicators except the collaboration of colleges with industries. The satisfaction level regarding the collaboration of colleges with industries is noted as the lowest for principals (1.62) and teachers (1.76), and for students, it is slightly higher (2.10 on a scale of 5). The lowest satisfaction level among students is recorded for connectivity through the internet (1.89), and the highest for teaching skills (3.92). The regression results show that for the full sample, the Difference-in-Difference (DiD) of the satisfaction scores on the quality of academic infrastructure, the quality of internet connection, and the quality of facilities for students’ soft skill improvement are statistically significant. The DiD for the other two satisfaction scores, namely, the teaching and learning environment and the degree of industry linkage, are not statistically significantly different from zero. These results show that the colleges that received Institutional Development Grants (IDGs) have made a positive and statistically significant impact on the improvement of the quality of academic infrastructure, quality of internet connection and other related facilities, and quality of facilities for students’ soft skill compared to those who did not receive this grant. However, the grant has made some changes in the teaching and learning environment and the degree of industry linkage between IDG awarded colleges and IDG non-recipient colleges. These changes are not statistically significant. The overall findings from the mid-term satisfaction survey highlighted that: (1) Institutional Development Grant (IDG) has made positive and statistically significant impact on the improvement of quality of academic infrastructure, quality of internet connection and other related facilities, and quality of facilities for students’ soft skill compared to those who did not receive this grant; (2) The grant has made some changes in the teaching and learning environment and the degree of industry linkage between IDG-awarded colleges and IDG non-recipient colleges. These changes are not significant enough to increase the satisfaction level of the students, teachers, and principals. Therefore, this study proposes these recommendations for increasing the overall satisfaction level of all stakeholders: (1) The poor level of industry collaboration has been highlighted by all types of beneficiaries. To facilitate industry collaboration, job fairs should be organised every year, preferably at the district level; (2) Introducing short course facilities can increase the job market opportunities of the NU-affiliated colleges; (3) Subject-based pedagogical training for the NU teachers is highly recommended; (4) The interrelation and collaboration between NU-affiliated colleges and universities should be increased. The colleges that are not well equipped with enough facilities can collaborate with the universities to share their equipment, such as computer labs, libraries, scientific labs, etc. This will help the less privileged colleges provide quality teaching and learning facilities to the students; (5) Forming and activating the activities of Alumni Associations in the NU-affiliated colleges; (6) There should be funds available for the renovation of old academic buildings, addition to an existing building, and upgrading labs and research facilities for teachers wherever appropriate, (7) There should be some provision of need-based funds/emergency grant that might be used or made available to the college authorities in case of sudden emergency or need (e.g., a sudden flash flood in Sylhet division)
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