Relevant bibliographies by topics / Training accuracy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Training accuracy'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Training accuracy"

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Stamoulis, Dean T., and Neil M. Hauenstein. "Rater training and rating accuracy: Training for dimensional accuracy versus training for ratee differentiation." Journal of Applied Psychology 78, no. 6 (1993): 994–1003. http://dx.doi.org/10.1037/0021-9010.78.6.994.

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Faust, D., T. J. Guilmette, K. Hart, H. R. Arkes, F. J. Fishburne, and L. Davey. "Neuropsychologists' training, experience, and judgment accuracy." Archives of Clinical Neuropsychology 3, no. 2 (1988): 145–63. http://dx.doi.org/10.1093/arclin/3.2.145.

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Faust, D. "Neuropsychologists' training, experience, and judgment accuracy." Archives of Clinical Neuropsychology 3, no. 2 (1988): 145–63. http://dx.doi.org/10.1016/0887-6177(88)90060-1.

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Libkuman, Terry M., Hajime Otani, and Neil Steger. "Training in Timing Improves Accuracy in Golf." Journal of General Psychology 129, no. 1 (2002): 77–96. http://dx.doi.org/10.1080/00221300209602034.

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WEBER, JUDITH L., ANN M. TINSLEY, LINDA B. HOUTKOOPER, and TIMOTHY G. LOHMAN. "Multimethod Training Increases Portion-Size Estimation Accuracy." Journal of the American Dietetic Association 97, no. 2 (1997): 176–79. http://dx.doi.org/10.1016/s0002-8223(97)00046-1.

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Yudho, Pijar Kuncoro, Nining Widyah Kusnanik, Oce Wiriawan, Mugiyo Hartono, and Sri Sumartiningsih. "Resistance circuit training and psychology increasing archery accuracy." Fizjoterapia Polska 25, no. 1 (2025): 364–68. https://doi.org/10.56984/8zg007d3mac.

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Aim of the study. The purpose of this study was to analyze the effect of resistance circuit training and psychological techniques on archery accuracy at distances of 50, 40, and 30 meters. This research was conducted as a quasi-experimental study using a matching-only design. Subjects were divided into two groups: (K1) athletes trained with mental imagery and circuit training using equipment, and (K2) athletes trained with mental imagery and circuit training without equipment. The intervention lasted for six weeks, with training sessions three times per week. Results. The findings of this study indicated significant differences between pre-test and post-test results in both groups (K1 and K2) (p < 0.05). The conclusion of this study suggests that resistance circuit training, both with and without equipment, combined with mental imagery training, can enhance archery accuracy. It is recommended that this type of training be incorporated into athletes' training programs. Future research should explore the integration of various training programs with more diverse and dynamic exercises.
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Pelangi, Gung Mas Goniiyun. "Pengaruh Pelatihan Small Sided Games terhadap Keterampilan Passing Control ditinjau dari Ketepatan pada Peserta Ekstrakurikuler Sepakbola SMK Restumuning." Jurnal Pendidikan Kesehatan Rekreasi Vol.9, No.2 (June 1, 2023): 236–45. https://doi.org/10.5281/zenodo.8099745.

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<em>The study focused on analyzing the difference found in the effect of small sided games training on passing control skills in terms of accuracy. The research used a quasi-experimental (quasi-experimental) used a treatment by level 2x2 design. There were 40 subjects determined using high and low accuracy categories. The data analysis technique used 2-way ANOVA at a significance level of 0,05. A passing control test was used as instrument. It was found that : 1) there were results differences between the small sided games training method on passing control skills in terms of accuracy with a significance of 0,001, 2) there were differences in results between the small sided games training method on passing control skills in terms of high accuracy with a significance of 0,003, 3) there were differences in results between the methods Small sided games training on passing control skills is viewed from low accuracy with a significance of 0,004 and 4) there was an interaction between the small sided games training method and accuracy on passing control skills with a significance of 0,023.</em>
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Santarcangelo, Maria, Robert A. Cribbie, and Amy S. Ebesu Hubbard. "Improving Accuracy of Veracity Judgment through Cue Training." Perceptual and Motor Skills 98, no. 3 (2004): 1039–48. http://dx.doi.org/10.2466/pms.98.3.1039-1048.

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Farid, Dewan Md, and Chowdhury Mofizur Rahman. "Assigning Weights to Training Instances Increases Classification Accuracy." International Journal of Data Mining & Knowledge Management Process 3, no. 1 (2013): 13–25. http://dx.doi.org/10.5121/ijdkp.2013.3102.

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Couture, Roger T., Mohan Singh, Wayne Lee, et al. "Can mental training help to improve shooting accuracy?" Policing: An International Journal of Police Strategies & Management 22, no. 4 (1999): 696–711. http://dx.doi.org/10.1108/13639519910299607.

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Dissertations / Theses on the topic "Training accuracy"

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Sinclair, Andrea L. "Differentiating Rater Accuracy Training Programs." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/35422.

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Prior investigation of a new rater training paradigm, rater variability training (RVT), found no clear empirical distinction between RVT and the more established frame-of-reference training (FOR), (Hauenstein, Facteau, & Schmidt, 1999). The purpose of the present study was to expand upon this previous investigation by including a purpose manipulation, alternative operationalizations of Cronbach's accuracy components, finer-grained distinctions in the rating stimuli, and a second control group receiving quantitative accuracy feedback void of a substantive training lecture. Results indicate that finer-grained distinctions in the rating stimuli result in the best differential elevation accuracy for RVT trainees. Furthermore, RVT may be best suited for improving raters' abilities to accurately evaluate average performing ratees when the performance appraisal is used for an administrative purpose. Evidence also suggests that in many cases, the use of Cronbach's accuracy components obscures underlying patterns of rating accuracy. Finally, there is evidence to suggest that accuracy feedback without a training lecture improves some types of rating accuracy.<br>Master of Science
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Jen, Mary. "Using training to increase accuracy of performance appraisals." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/29569.

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Lalonde, Kathryn Mackenzie. "Improving manual interception accuracy through eye and hand training." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54148.

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Accurate hand movements are important for many daily activities and we frequently use vision to help guide our interactions with our environment. Here we investigated whether smooth pursuit training transfers to hand movements by examining manual interception accuracy. We conducted three series of five-day perceptual-motor learning experiments. In a track-intercept task, observers were instructed to track a moving target on a screen and to hit it with their index finger as soon as it entered a “hit zone”. In each trial, only the first part (100-300 ms) of the trajectory was shown and observers had to extrapolate and intercept the target at its assumed position. In all three experiments, subjects were tested on an eye-hand coordination task on the first day (day 1, pre-test) and last day (day 5, post-test); the three experiments differed with regard to training on days 2-4. Further, subjects were invited to complete the eye-hand coordination task during a one-week follow-up session after the post-test (day 6). Experiment 1 (n=9) involved no hand movements during training; subjects only tracked the target with their eyes and received no visual feedback. Subjects in Experiment 2 (n=9) tracked and intercepted the target during training. Experiment 3 (n=9) served as a control and involved no training. Subjects in all groups were invited to come back one week after the post-test for a follow-up testing session. Results show that manual interception performance (finger position error) improves in all groups, but improves most following combined eye-hand training. Interestingly, this group also resulted in the greatest improvement in eye movements. This finding is particularly noteworthy because both training groups involved the same degree of eye-movement training, but eye movements improved only if combined with engaging the hand. Analysis of performance in the one week follow-up after the post-test revealed that training effects in the eye-hand group were particularly long-lasting and stable, whereas eye movements continued to improve through to the week follow-up. I will discuss implications of these results for our understanding of the brain pathways underlying eye and hand movement control, as well as practical applications in sports and clinical rehabilitation.<br>Medicine, Faculty of<br>Graduate
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Wessel, Paige. "Accuracy of Commercial Fitness Trackers During High-Intensity Functional Training." TopSCHOLAR®, 2016. http://digitalcommons.wku.edu/theses/1634.

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Commercially available fitness trackers have been found to accurately measure steps and caloric expenditure during walking and running activities. Circuit-style, highintensity functional training (HIFT) has become increasingly popular because it is inexpensive and effective in improving muscular strength and cardiovascular fitness. PURPOSE: To evaluate the accuracy of five accelerometers (ActiGraph GT3X, Nike Fuelband, Fitbit One, Fitbit Charge HR, and Jawbone UP Move) in estimating energy expenditure while performing an acute bout of HIFT. METHODS: Participants (n = 47) underwent baseline testing and at least 48 hours later, each participant completed the main test: a 15-minute workout consisting of 12 repetitions each of 7 different exercises; performed circuit-style by completing as many rounds as possible. During the main test, each participant wore the Cosmed K4b2 portable metabolic analyzer (PMA) and five different accelerometers. RESULTS: Four of the five fitness trackers reported lower (p <0.01) total caloric expenditure values compared to the PMA during the acute bout of HIFT. The waist-mounted device (ActiGraph, 182.55 ± 37.93 kcals) most closely mimicked caloric expenditure compared to the PMA (Cosmed, 144.99 ± 37.13 kcals) as indicated by an insignificant p value (0.056). Systematic differences between the activity monitors were calculated using an Intraclass Correlation (ICC) with an ICC = -0.032. The ICC of F (46,235) = 0.812 (p = 0.799) was not significant at the predetermined 0.05 alpha level. A Repeated Measures ANOVA showed that when compared to the Cosmed, all activity monitors were significantly different at the 0.05 alpha level. The Fitbit One and the Fitbit Charge HR were the only two activity monitors that are not significantly different from one another (p = 0.985). The range of error based on mean absolute percentage errors (MAPE) was lowest for the ActiGraph (15.1%) and highest for the Fitbit Charge HR (22.1%). CONCLUSION: The wrist- and hip-mounted fitness trackers do not accurately assess energy expenditure during HIFT exercise. Supported by: WKU Graduate School, NIGMS 2P20 GM103436-14; Institutional Development Award (IDeA) from National Institute of General Medical Sciences, National Institutes of Health, 5P20GM103436 and the WKU RCAP Grant 14-8007.
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Yonge, Katherine Chandler. "Criminal profile accuracy following training in inductive and deductive approaches." Master's thesis, Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-03312008-194642.

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Gorman, Charles Allen, and Joan R. Rentsch. "Retention of Assessment Center Rater Training." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/535.

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The purpose of this research was to examine frame-of-reference (FOR) training retention in an assessment center (AC) rater training context. In this study, we extended Gorman and Rentsch’s (2009) research showing FOR training effects on performance schemas by examining the effects immediately after training and again after a two-week nonuse period. We examined the retention effects of FOR training on performance ratings and on performance schema accuracy. The results indicated that the FOR training condition, compared to a control condition, yielded performance ratings and performance schemas more similar to expert ratings and to an expert schema, respectively. FOR training also had positive effects on ratings and performance schema accuracy assessed two weeks after training. These results support and extend the theory of FOR training, which posits that the instructed theory of performance replaces the preexisting rater schemas (Lievens, 2001), and they contribute to the research on FOR training within AC contexts.
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Anderson, Jesse Glenn Sigrid S. "An examination of the effects of accuracy+rate versus accuracy+observing response training methods on matching-to-sample performance." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3708.

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Anderson, Jesse. "An examination of the effects of accuracy+rate versus accuracy+observing response training methods on matching-to-sample performance." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc3708/.

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The relative efficacy of training procedures emphasizing accuracy versus those which add a rate criterion is a topic of debate. The desired learning outcome is fluent responding, assessed by measures of retention, endurance, stability, and application. The current study examined the effects of these two procedures on fluency outcomes using a matching-to-sample paradigm to train participants to match English to Japanese characters. An explicit FR-3 observing response was added to an accuracy-only condition to assess the extent to which it may facilitate learning. Total time spent responding in practice drills in accuracy-only conditions was yoked to total time spent in drills achieving rate aims in accuracy+rate (AR) conditions. One participant clearly demonstrated superior fluency outcomes after AR training while another displayed superior endurance and stability outcomes after such training. The remaining two participants did not demonstrate significantly different fluency outcomes across conditions.
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Eyles, Kieren. "The empathy fillip : can training in microexpressions of emotion enhance empathic accuracy?" Thesis, University of Roehampton, 2016. https://pure.roehampton.ac.uk/portal/en/studentthesis/the-empathy-fillip(972e0b0a-0669-476b-b869-26247c51ecd2).html.

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Empathy is a central concern in the counselling process. Though much researched, and broadly commented upon, empathy is still largely understood through the words within a client-counsellor interaction. This semantic focus continues despite converging lines of evidence that suggest other elements of an interaction – for example body language – may be involved in the communication of empathy. In this thesis, the foundations of empathy are examined, focusing on empathy’s professional instantiation. These foundations are then related to the idea that the face, and its ability to express emotion, are an important part of the empathic process. What follows is an experiment testing 60 participants. This was a between groups design, with participants assigned to two even groups; one group receiving training in how emotion appears on the face: using the training program eMETT; the other reading a passage on empathy. Following the intervention, hypothesised group differences were assessed using the following analyses. Firstly, an Independent sample T-test, compared group means on the Ickes Empathic Accuracy paradigm, the measure of empathy used. Secondly, a further Independent sample T-test assessed the effect of eMETT training. Thirdly, an ANCOVA, evaluated whether the obtained results may have been confounded by age difference between the experimental groups. Finally a correlational analyse tested for any relationship between baseline and outcome measures. The hypothesis tested stated: training in facial expressions of emotion will enhance counsellors’ empathic accuracy; a hypothesis for which positive evidence was shown. The implications of this evidence suggest efficacy of the eMETT training to enhance empathic accuracy, though this is qualified through critical examination of the experimental method. Suggestions for refinement of this method are discussed.
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Keown, Janine. "The effects of task information training and frame-of-reference training with situational constraints on rating accuracy." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24673.pdf.

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Books on the topic "Training accuracy"

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Goldsmith, Martin. Applying the national training center experience: Artillery targeting accuracy. Rand, 1990.

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Jones, Lolita M. Mastering the chargemaster: Training for a team-based approach to accuracy. HCPro, 2004.

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Crane, Peter M. Flight training simulators: Effects of terrain accuracy on simulated radar image quality. Air Force Human Resources Laboratory, Air Force Systems Command, 1985.

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L, Dickinson Terry, and Air Force Human Resources Laboratory., eds. Work performance ratings: Cognitive modeling and feedback principles in rater accuracy training. Air Force Systems Command, Air Force Human Resources Laboratory, 1990.

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Doherty, Meghan C. Engraving Accuracy in Early Modern England. Amsterdam University Press, 2022. http://dx.doi.org/10.5117/9789463721066.

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The book traces major concepts including: the creation of the visual effects of accuracy through careful action and training; the development of visual judgment and connoisseurship; the role of a network in the production of knowledge; balancing readers’ expectations with representational conventions; and the effects of acts of collecting on the creation and circulation of knowledge. On the one hand, this study uncovers that approaches to knowledge production were different in the seventeenth century, as compared with in the twenty-first century. On the other, it reveals how the early modern struggle to sort through an overwhelming quantity of visual information - brought on by major changes in image production and circulation - resonates with our own.
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United States. Employment and Training Administration, ed. UI PERFORMS BENEFIT... ACCURACY MEASUREMENT... CY 1996 ANNUAL REPORT... UNEMPLOYMENT INSURANCE SERVICE... EMPLOYMENT & TRAINING ADMIN.... U. s.n., 1998.

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Holakovsky, Barbara. Evaluation and management training handbook: Document for accurate code selection. HCPro, 2004.

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Shalimov, Mihail, Andrey Fiveyskiy, and Ekaterina Votinova. The basics of technological preparation of production. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1027837.

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The tutorial provides basic information about the purpose, content, basic principles and organization of technological preparation of welding production. The types of technical documents, their purpose and content; fundamentals of design of technological process of manufacture of welded structures. Special attention is paid to the optimisation of the design for manufacturability.&#x0D; A careful study of the textbook material will prepare the student for designing welded structures with the required accuracy and reliability at a given performance and economic efficiency, as well as development of technological processes of welding, providing the weld joints with the desired properties of reliability and safe operation of welded products.&#x0D; Designed for students training areas 15.03.01 mechanical engineering, 12.03.05 "Laser engineering and laser technologies" may be useful to students of lyceums, colleges, vocational schools and technical schools.
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Galtinovskiy, Andrey, Sergey Bochkarev, and Mihail Abashin. Theoretical foundations of rocket and space technology production. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/1959273.

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The textbook contains material for studying the theoretical foundations of the technology of production of rocket and space technology, including the features of its manufacture and the organization of technical training. Methods for determining errors and their calculations in the processing of parts, achieving processing accuracy, calculating allowances are considered. The theoretical principles of designing technological processes and registration of technological documentation are given. The technological foundations of increasing labor productivity and the degree of perfection and progressiveness of the technological process are analyzed. The textbook is based on the material of lectures given by the authors to students of Bauman Moscow State Technical University (National Research University) and Perm National Research Polytechnic University. Meets the requirements of the latest generation of federal state standards of higher education. It is intended for teachers, students and postgraduates of rocket engineering specialties of technical and military higher educational institutions.
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Office, General Accounting. Tax administration: Improvement in IRS' telephone assistor accuracy : fact sheet for the Chairman, Subcommittee on Commerce, Consumer, and Monetary Affairs, Committee on Government Operations, House of Representatives. The Office, 1992.

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Book chapters on the topic "Training accuracy"

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Love, Carrie. "Medical Accuracy on Set." In The Power of Virtual Reality Cinema for Healthcare Training. Productivity Press, 2021. http://dx.doi.org/10.4324/9781003168683-19.

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Juozapavičius, Aušrius, Stefan Sütterlin, and Ricardo G. Lugo. "Towards a Metacognitive Accuracy Training in Military Education." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06388-6_20.

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Mechouma, Toufik, Ismail Biskri, Jean Guy Meunier, and Alaidine Ben Ayed. "Cbow Training Time and Accuracy Optimization Using SkipGram." In Advances in Computational Collective Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88113-9_46.

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Nurlanov, Zhakshylyk, Frank R. Schmidt, and Florian Bernard. "Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70371-3_8.

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Haben, Stephen, Marcus Voss, and William Holderbaum. "Load Forecasting Model Training and Selection." In Core Concepts and Methods in Load Forecasting. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27852-5_8.

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AbstractChapters 5 and 6 have shown how to define a time series forecast, how to prepare the data, and how to generate inputs for the models. Chapters 9 to 11 will show several methods for forecasting the demand. However, although Chap. 7 provided us the tools for measuring the accuracy of a forecast, the following questions remain largely unanswered: How do we train and select a model which will consistently produce accurate forecasts?. This chapter will investigate this question by looking at some of the most important aspects for creating a good forecast including proper utilisation of benchmarking, and how to use cross-validation to properly train your model. Underlying cross-validation is one of the most important aspects of a creating a good forecast, the so-called bias-variance trade-off principle, discussed in Sect. 8.1.2. This ensures that the model is not over (or under-) trained and allows the model to better generalise to new, unseen data.
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Stawicki, Piotr, Aya Rezeika, and Ivan Volosyak. "Effects of Training on BCI Accuracy in SSMVEP-based BCI." In Advances in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85099-9_6.

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Zhang, Ruihan, and Jun Sun. "Certified Robust Accuracy of Neural Networks Are Bounded Due to Bayes Errors." In Computer Aided Verification. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65630-9_18.

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AbstractAdversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there is a certain fundamental limit on achieving robustness whilst maintaining accuracy. In this work, we offer a novel perspective based on Bayes errors. By adopting Bayes error to robustness analysis, we investigate the limit of certified robust accuracy, taking into account data distribution uncertainties. We first show that the accuracy inevitably decreases in the pursuit of robustness due to changed Bayes error in the altered data distribution. Subsequently, we establish an upper bound for certified robust accuracy, considering the distribution of individual classes and their boundaries. Our theoretical results are empirically evaluated on real-world datasets and are shown to be consistent with the limited success of existing certified training results, e.g., for CIFAR10, our analysis results in an upper bound (of certified robust accuracy) of 67.49%, meanwhile existing approaches are only able to increase it from 53.89% in 2017 to 62.84% in 2023.
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Mason, Julian D., Kateřina Hlaváčková, and Kevin Warwick. "Approximation Using Cubic B-Splines with Improved Training Speed and Accuracy." In Computer Intensive Methods in Control and Signal Processing. Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-1996-5_19.

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Korovkinas, Konstantinas, Paulius Danėnas, and Gintautas Garšva. "SVM Accuracy and Training Speed Trade-Off in Sentiment Analysis Tasks." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99972-2_18.

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Matsuno, Ryuta. "GBCE: Enhanced Training Loss to Estimate Accuracy of Models in Production." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-8180-8_3.

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Conference papers on the topic "Training accuracy"

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Chen, Li-Kuang, Canasai Kruengkrai, and Junichi Yamagishi. "Outlier-Aware Training for Improving Group Accuracy Disparities." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop. Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-srw.8.

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Fan, Chunlong, Wanyan Guo, and Li Xu. "Enhancing Model Robustness and Accuracy via Learnable Adversarial Training." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033255.

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Deng, Yi, Yuanli Zhang, and Jingrui Zhang. "The Influence of Mamba Model Hyperparameters on Training Time and Accuracy." In 2024 IEEE International Conference on Smart Internet of Things (SmartIoT). IEEE, 2024. https://doi.org/10.1109/smartiot62235.2024.00052.

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Akyürek, Afra Feyza, Ekin Akyürek, Leshem Choshen, Derry Wijaya, and Jacob Andreas. "Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.584.

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Pannir Selvam, Durai Arun, Alan Wilmshurst, Kevin Thomas, and Gaetano Di Caterina. "Bimodal accuracy distribution in quantisation aware training of SNNs: an investigation." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf. SPIE, 2024. http://dx.doi.org/10.1117/12.3033999.

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Uchida, Hironori, Keitaro Tominaga, Hideki Itai, Yujie Li, and Yoshihisa Nakatoh. "Log Parameter Anomaly Detection System: Evaluating Accuracy with Noisy Training Data." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930057.

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Naeini, Mohammadreza Tavasoli, Ali Bereyhi, Morteza Noshad, Ben Liang, and Alfred O. Hero. "Universal Training of Neural Networks to Achieve Bayes Optimal Classification Accuracy." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890777.

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Kim, Youngseo, Cimang Lu, Xiang Qiu, and Yi Zhao. "Improving Processing-In-Memory Chip Inferencing Accuracy Through Noise-Aware Training." In 2025 8th World Conference on Computing and Communication Technologies (WCCCT). IEEE, 2025. https://doi.org/10.1109/wccct65447.2025.11027943.

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Nguyen, Truong-Son, Dinh-Khoi Pham, and Thai-Son Tran. "Multi-Head Training Boosting: A Training Method to Increase the Accuracy of Low-Parameter Models in Multiple Object Tracking." In 2024 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908319.

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Tripathi, Ashutosh, Apar Tyagi, Aditya Dubey, Surbhi Vijh, and Pradeep Verma. "Enhancing Virtual Yoga Training: Real-time Pose Assessment with Voice-guided Accuracy Feedback." In 2025 International Conference on Engineering, Technology & Management (ICETM). IEEE, 2025. https://doi.org/10.1109/icetm63734.2025.11051473.

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Reports on the topic "Training accuracy"

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Garlinger, Delane K. The Effectiveness of a Rater Training Booklet in Increasing Accuracy of Performance Ratings. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada194723.

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Bhushan, Shanti, Greg Burgreen, Wesley Brewer, and Ian Dettwiller. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49702.

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A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the machine-learned model from over training on more prevalent flow regimes, and results in a model with similar accuracy. Turbulence production is correlated with shear strain in the free-shear region, with shear strain and wall-distance and local velocity-based Reynolds number in the boundary layer regime, and with streamwise velocity gradient in the accelerating flow regime. The flow direction is key in identifying flow separation and reattachment regime. Machine-learned models perform poorly in extrapolation mode. A priori tests reveal model predictability improves as the hill dataset is partially added during training in a partial extrapolation model. These also provide better turbulent kinetic energy and shear stress predictions than RANS in a posteriori tests. Before a machine-learned model is applied for a posteriori tests, a priori tests should be performed.
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Farria, Dione M. Impact of Feature-Based Training and Auditing on Diagnostic Accuracy and Agreement in Mammographic Interpretations. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada390813.

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Cesare, Steven J., and Terry L. Dickinson. The Effects of Rater Training and Practice and Feedback on the Accuracy of Behavioral Observation and Performance Ratings. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada303762.

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculation of recognition error and training accuracy are presented.
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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.
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Maloney, Megan, Sarah Becker, Andrew Griffin, Susan Lyon, and Kristofer Lasko. Automated built-up infrastructure land cover extraction using index ensembles with machine learning, automated training data, and red band texture layers. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49370.

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Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to separate built-up infrastructure from spectrally similar bare ground. Our evaluation produced global thresholds by evaluating ground truth points against a range of site-specific optimal index thresholds across the 24 images. These were used to classify an ensemble, and then spectral indexes, texture, and stratified random sampling guided training data selection. The training data fit a random forest classifier to create final binary maps. Validation found an average overall accuracy of 79.95% (±4%) and an F1 score of 0.5304 (±0.07). The inclusion of the texture metric improved overall accuracy by 14–21%. A comparison to site-specific thresholds and a deep learning-derived layer is provided. This automated built-up infrastructure mapping framework requires only public imagery to support time-sensitive land management workflows.
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Suir, Glenn, Christina Saltus, Charles Sasser, et al. Evaluating drone truthing as an alternative to ground truthing : an example with wetland plant identification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42201.

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Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.
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Suir, Glenn, Christina Saltus, Charles Sasser, et al. Evaluating drone truthing as an alternative to ground truthing : an example with wetland plant identification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42201.

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Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.
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Goodwin, Peter, and Rebecca Molinari. Phragmites Mapping and Modeling in Great Salt Lake Wetlands at the Bear River Migratory Bird Refuge, Utah. Utah Geological Survey, 2024. http://dx.doi.org/10.34191/ri-287.

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Phragmites (Phragmites australis ssp. australis) threatens Great Salt Lake wetlands by readily displacing desirable native vegetation, degrading habitats, and diverting water from the lake and surrounding wetlands through increased evapotranspiration water loss. Understandably, land managers around the Great Salt Lake consider phragmites control a top priority and invest significant resources to treat and eradicate phragmites. Effective phragmites control relies on accurate mapping to identify possible treatment and follow up areas, and to clearly distinguish between phragmites and native vegetation to minimize overspray during herbicide treatments. Lake-wide mapping has not been widely completed since 2014, and managers need mapping that reflects the current phragmites extent and that can be easily updated to keep pace with phragmites expansion and ongoing control. The Utah Division of Forestry, Fire &amp; State Lands (FFSL) recognizes these challenges and intends to develop a multitiered mapping approach to capture small infestations, gauge treatment success across management areas, and quantify phragmites extent across the entire Great Salt Lake. To be successful, this approach requires accurate phragmites mapping to train and validate the models. The Utah Geological Survey (UGS) developed this project with FFSL and the U.S. Fish and Wildlife Service (USFWS) to provide current and accurate phragmites mapping as training data for the FFSL models while also providing the Bear River Migratory Bird Refuge (Refuge) with an updated vegetation map. We mapped vegetation on the Refuge with two approaches— a manual photo interpretation approach using high-resolution satellite imagery collected July 2023 and a machine-learning approach using a random forest classifier. Both approaches completely mapped existing phragmites infestations and treated phragmites across a project area defined by five highpriority management units on the Refuge. For the manual mapping, we identified additional vegetation communities in the project area to align with USFWS management objectives. We assessed the accuracy of each approach with validation data and confusion matrices to calculate overall accuracy and phragmites-specific accuracies. We collected two sets of validation data: (1) field points representing on-the-ground conditions in early November 2023, and (2) a withheld subset of the data used to train each random forest model. We further tested automated phragmites mapping approaches by varying several model inputs (imagery resolution, month of imagery collection, and training data collection type) and evaluated resulting model accuracies.
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