Relevant bibliographies by topics / Output error

Contents

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

Academic literature on the topic 'Output error'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Output error"

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TOMITA, YUTAKA, AD A. H. DAMEN, and PAUL M. J. VAN DEN HOF. "Equation error versus output error methods." Ergonomics 35, no. 5-6 (May 1992): 551–64. http://dx.doi.org/10.1080/00140139208967836.

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Popović, Maja, and Hermann Ney. "Towards Automatic Error Analysis of Machine Translation Output." Computational Linguistics 37, no. 4 (December 2011): 657–88. http://dx.doi.org/10.1162/coli_a_00072.

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Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to classify translation errors in many different ways. This work focuses on one possible set-up, namely, on five error categories: inflectional errors, errors due to wrong word order, missing words, extra words, and incorrect lexical choices. For each of the categories, we analyze the contribution of various POS classes. We compared the results of automatic error analysis with the results of human error analysis in order to investigate two possible applications: estimating the contribution of each error type in a given translation output in order to identify the main sources of errors for a given translation system, and comparing different translation outputs using the introduced error categories in order to obtain more information about advantages and disadvantages of different systems and possibilites for improvements, as well as about advantages and disadvantages of applied methods for improvements. We used Arabic–English Newswire and Broadcast News and Chinese–English Newswire outputs created in the framework of the GALE project, several Spanish and English European Parliament outputs generated during the TC-Star project, and three German–English outputs generated in the framework of the fourth Machine Translation Workshop. We show that our results correlate very well with the results of a human error analysis, and that all our metrics except the extra words reflect well the differences between different versions of the same translation system as well as the differences between different translation systems.
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Cui, Pengcheng, Bin Li, Jing Tang, Jiangtao Chen, and Youqi Deng. "A modified adjoint-based grid adaptation and error correction method for unstructured grid." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840020. http://dx.doi.org/10.1142/s0217984918400201.

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Grid adaptation is an important strategy to improve the accuracy of output functions (e.g. drag, lift, etc.) in computational fluid dynamics (CFD) analysis and design applications. This paper presents a modified robust grid adaptation and error correction method for reducing simulation errors in integral outputs. The procedure is based on discrete adjoint optimization theory in which the estimated global error of output functions can be directly related to the local residual error. According to this relationship, local residual error contribution can be used as an indicator in a grid adaptation strategy designed to generate refined grids for accurately estimating the output functions. This grid adaptation and error correction method is applied to subsonic and supersonic simulations around three-dimensional configurations. Numerical results demonstrate that the sensitive grids to output functions are detected and refined after grid adaptation, and the accuracy of output functions is obviously improved after error correction. The proposed grid adaptation and error correction method is shown to compare very favorably in terms of output accuracy and computational efficiency relative to the traditional featured-based grid adaptation.
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HAMIDI, HODJAT, ABBAS VAFAEI, and SEYED AMIRHASSAN MONADJEMI. "ANALYSIS AND DESIGN OF AN ABFT AND PARITY-CHECKING TECHNIQUE IN HIGH PERFORMANCE COMPUTING SYSTEMS." Journal of Circuits, Systems and Computers 21, no. 03 (May 2012): 1250017. http://dx.doi.org/10.1142/s021812661250017x.

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We present a new approach to algorithm-based fault tolerance (ABFT) and parity-checking techniques in the design of high performance computing systems. The ABFT technique employs real convolution error-correcting codes to encode the input data. In order to reduce the round-off error from the output decoding process, systematic real convolution encoding is employed. This paper proposes an efficient method to detect the arithmetic errors using convolution codes at the output compared with an equivalent parity value derived from the input data. Number data processing errors are detected by comparing parity values associated with a convolution code. These comparable sets will be very close numerically, although not identical because of round-off error differences between the two parity generation processes. The effects of internal failures and round-off error are modeled by additive error sources located at the output of the processing block and input at threshold detector. This model combines the aggregate effects of errors and applies them to the respective outputs.
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Windridge, David, Riccardo Mengoni, and Rajagopal Nagarajan. "Quantum error-correcting output codes." International Journal of Quantum Information 16, no. 08 (December 2018): 1840003. http://dx.doi.org/10.1142/s0219749918400038.

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Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.
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Escalera, Sergio, David Masip, Eloi Puertas, Petia Radeva, and Oriol Pujol. "Online error correcting output codes." Pattern Recognition Letters 32, no. 3 (February 2011): 458–67. http://dx.doi.org/10.1016/j.patrec.2010.11.005.

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Rosenqvist, Fredrik, and Anders Karlström. "Piecewise-Linear Output-Error Models." IFAC Proceedings Volumes 36, no. 16 (September 2003): 1795–800. http://dx.doi.org/10.1016/s1474-6670(17)35020-6.

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Sasson, Ariella, and Todd P. Michael. "Filtering error from SOLiD Output." Bioinformatics 26, no. 6 (March 15, 2010): 849–50. http://dx.doi.org/10.1093/bioinformatics/btq045.

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Poladi, Irfan, and Hitesh Ishwardas. "Review paper on Error Correcting Output Code Based on Multiclass Classification." International Journal of Scientific Research 2, no. 2 (June 1, 2012): 134–36. http://dx.doi.org/10.15373/22778179/feb2013/45.

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Gopalratnam, G., and J. R. Raol. "Analysis of stabilised output error methods." IEE Proceedings - Control Theory and Applications 143, no. 2 (March 1, 1996): 209–17. http://dx.doi.org/10.1049/ip-cta:19960195.

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Dissertations / Theses on the topic "Output error"

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Tohme, Elie. "Initialization of output error identification algorithms." Poitiers, 2008. http://www.theses.fr/2008POIT2259.

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Cette thèse traite de l'initialisation des algorithmes à erreur de sortie pour approcher la convergence globale. Deux approches sont proposées. La première s'appuie sur les moments partiels réinitialisés. Cette approche de type erreur d'équation est comparée à d'autres méthodes par simulation stochastique dans les cas discrets et continus. Les résultats obtenus montrent l'efficacité des moments partiels réinitialisés pour l'initialisation des algorithmes à erreur de sortie. Dans la deuxième approche, les algorithmes proposés, dits à pseudo-erreur de sortie, sont construits à partir des méthodes standards d'erreur de sortie en tentant de respecter une hypothèse de passivité qui assure la convergence globale. Malgré un biais sur les paramètres obtenus, ceux-ci fournissent une bonne initialisation des algorithmes à erreur de sortie
This thesis deals with the initialization of output error identification algorithms in order to achieve the global convergence. Two approaches are proposed. The first approach is based on the reinitialized partial moments. This equation error type approach is compared with several methods using stochastic simulations in both discrete and continuous domains. The obtained results show the efficiency of the reinitialized partial moments to initialize output error algorithms. In the second approach, the proposed algorithms, named pseudo-output error algorithms, are built from the standard output error methods by trying to satisfy a particular positive realness condition that assures the global convergence. Despite the bias of the estimated parameters, these approaches provide a good initialization to the output error algorithms
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Tidefelt, Henrik. "Applied Output Error Identification: SI Engine Under Normal Operating Conditions." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2380.

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This report presents work done in the field of output error identification, with application to spark ignition (SI) engine identification for the purpose of air to fuel ratio control. The generic parts of the project consist mainly in setting out the basis for the design of output error identification software. Efficiency issues related to linear state space models have also been explored, and although the software design is not made explicit in this report, many of the important concepts have been implemented in order to provide powerful abstractions for the application to SI engine identification.

The SI engine identification data was obtained under normal operating conditions. The goal is to re- estimate models without utilizing a virtual measurement which has been used successfully to estimate models in the past. This turns out to be a difficult problem much related to the lack of excitation in the system input, shortcomings of the fuel dynamics model and the unknown and hard to estimate exhaust sensor characteristics. Indeed, the larger of the previously estimated models are found not to be identifiable in the present situation. However, trivial restrictions of the models (not meaning restriction to trivial models) avoid that problem.

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Carew, Warren D. "Multivariable control of a nonlinear process : output prioritisation by error redistribution." Doctoral thesis, University of Cape Town, 2002. http://hdl.handle.net/11427/5255.

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Carlstedt, Tobias. "Algorithms for analysis of GSM phones’ modulation quality." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17248.

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Zorian, Yervant. "Optimized error coverage in built-in self-test by output data modification." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=75778.

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The concept of Built-In Self-Test (BIST) has recently become an increasingly attractive solution to the complex problem of testing VLSI chips. However, the realization of BIST faces some challenging problems of its own. One of these problems is to increase the quality of fault coverage of a BIST implementation, without incurring a large overhead. In particular, the loss of information in the output data compressor, which is typically a multi-input linear feedback shift register (MISR), is a major cause of concern.
In the recent past, several researchers have proposed different schemes to reduce this loss of information, while maintaining the need for a small area overhead.
In this dissertation, a new BIST scheme, based on modifying the output data before compression, is developed. This scheme, called output data modification (ODM), exploits the knowledge of the functionality of the circuit under test to provide a circuit-specific BIST structure. This structure is developed so that it can conveniently be implemented for any general circuit under consideration. But more importantly, a proof of effectiveness is provided to show that ODM will, on the average, be orders of magnitude better than all existing schemes in its capability to reduce the information loss, for a given amount of area overhead.
Moreover, the constructive nature of the proof will allow one to provide a simple trade-off between the reduction tolerated in information loss to the area overhead needed to affect this reduction.
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Elliott, Debra. "Corpus-based machine translation evaluation via automated error detection in output texts." Thesis, University of Leeds, 2006. http://etheses.whiterose.ac.uk/221/.

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Since the emergence of the first fully automatic machine translation (MT) systems over fifty years ago, the use of MT has increased dramatically. Consequently, the evaluation of MT systems is crucial for all stakeholders. However, the human evaluation of MT output is expensive and time-consuming, often relying on subjective quality judgements and requiring human `reference translations' against which the output is compared. As a result, interest in more recent years has turned towards automated evaluation methods, which aim to produce scores that reflect human quality judgements. As the majority of published automated evaluation methods still require human `reference translations' for comparison, the goal of this research is to investigate the potential of a method that requires access only to the translation. Based on detailed corpus analyses, the primary aim is to devise methods for the automated detection of particular error types in French-English MT output from competing systems and to explore correlations between automated error counts and human judgements of a translation as a whole. First, a French-English corpus designed specifically for MT evaluation was compiled. A sample of MT output from the corpus was then evaluated by humans to provide judgements against which automated scores would ultimately be compared. A datadriven fluency error classification scheme was subsequently developed to enable the consistent manual annotation of errors found in the English MT output, without access to the original French text. These annotations were then used to guide the selection of error categories for automated error detection, and to facilitate the analysis of particular error types in context so that appropriate methods could be devised. Manual annotations were further used to evaluate the accuracy of each automated approach. Finally, error detection algorithms were tested on English MT output from German, Italian and Spanish to determine the extent to which methods would need to be adapted for use with other language pairs.
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Cuéllar, Fuentes Jesús Seaman John W. Tubbs Jack Dale. "Statistical monitoring of a process with autocorrlated output and observable autocorrelated measurement error." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5185.

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Luo, Yurong. "The severity of stages estimation during hemorrhage using error correcting output codes method." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/406.

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As a beneficial component with critical impact, computer-aided decision making systems have infiltrated many fields, such as economics, medicine, architecture and agriculture. The latent capabilities for facilitating human work propel high-speed development of such systems. Effective decisions provided by such systems greatly reduce the expense of labor, energy, budget, etc. The computer-aided decision making system for traumatic injuries is one type of such systems that supplies suggestive opinions when dealing with the injuries resulted from accidents, battle, or illness. The functions may involve judging the type of illness, allocating the wounded according to battle injuries, deciding the severity of symptoms for illness or injuries, managing the resources in the context of traumatic events, etc. The proposed computer-aided decision making system aims at estimating the severity of blood volume loss. Specifically speaking, accompanying many traumatic injuries, severe hemorrhage, a potentially life-threatening condition that requires immediate treatment, is a significant loss of blood volume in process resulting in decreased blood and oxygen perfusion of vital organs. Hemorrhage and blood loss can occur in different levels such as mild, moderate, or severe. Our proposed system will assist physicians by estimating information such as the severity of blood volume loss and hemorrhage , so that timely measures can be taken to not only save lives but also reduce the long-term complications as well as the cost caused by unmatched operations and treatments. The general framework of the proposed research contains three tasks and many novel and transformative concepts are integrated into the system. First is the preprocessing of the raw signals. In this stage, adaptive filtering is adopted and customized to filter noise, and two detection algorithms (QRS complex detection and Systolic/Diastolic wave detection) are designed. The second process is to extract features. The proposed system combines features from time domain, frequency domain, nonlinear analysis, and multi-model analysis to better represent the patterns when hemorrhage happens. Third, a machine learning algorithm is designed for classification of patterns. A novel machine learning algorithm, as a new version of error correcting output code (ECOC), is designed and investigated for high accuracy and real-time decision making. The features and characteristics of this machine learning method are essential for the proposed computer-aided trauma decision making system. The proposed system is tested agasint Lower Body Negative Pressure (LBNP) dataset, and the results indicate the accuracy and reliability of the proposed system.
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Park, Jong Il. "Trellis based soft output decoding algorithms for concatenated coding systems." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/13898.

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Potter, Chris. "Modeling Channel Estimation Error in Continuously Varying MIMO Channels." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604490.

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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada
The accuracy of channel estimation plays a crucial role in the demodulation of data symbols sent across an unknown wireless medium. In this work a new analytical expression for the channel estimation error of a multiple input multiple output (MIMO) system is obtained when the wireless medium is continuously changing in the temporal domain. Numerical examples are provided to illustrate our findings.
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Books on the topic "Output error"

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Chen, S. A prediction-error estimation algorithm for nonlinear output-affine systems. Sheffield: University of Sheffield, Dept. of Control Engineering, 1987.

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Maine, Richard E. Application of parameter estimation to aircraft stability and control: The output-error approach. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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S, Madheswaran, and Institute for Social and Economic Change, eds. Casuality between energy consumption and output growth in Indian cement industry: An application of panel vector error correction model. Bangalore: Institute for Social and Economic Change, 2010.

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Siegel, Donald S. Errors in output deflators revisited: Unit values and the PPI. Cambridge, MA: National Bureau of Economic Research, 1991.

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Prol, Marta Pombo. Error and text analysis of MT outputs for post-edition purposes. Manchester: UMIST, 1996.

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The software developer's sourcebook: From concept to completion : the essential reference. Reading, Mass: Addison-Wesley Pub. Co., 1985.

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Devito, Kevin John. Errors in estimating stream discharge in small headwater catchments: Influence on interpretation of catchment yields and input-output budget estimates. [Toronto]: Queen's Printer for Ontario, 1993.

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Kong, Ŭn-bae. Understanding and improving error-correcting output coding. 1995.

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W, Iliff Kenneth, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., eds. Application of parameter estimation to aircraft stability and control: The output-error approach. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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W, Iliff Kenneth, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Branch., eds. Application of parameter estimation to aircraft stability and control: The output-error approach. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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Book chapters on the topic "Output error"

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Langford, John, Xinhua Zhang, Gavin Brown, Indrajit Bhattacharya, Lise Getoor, Thomas Zeugmann, Thomas Zeugmann, et al. "Error Correcting Output Codes." In Encyclopedia of Machine Learning, 331. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_260.

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Escalera, Sergio, Oriol Pujol, and Petia Radeva. "Recoding Error-Correcting Output Codes." In Multiple Classifier Systems, 11–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_2.

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Langford, John, and Alina Beygelzimer. "Sensitive Error Correcting Output Codes." In Learning Theory, 158–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_11.

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Milanesi, Carlo. "Input/Output and Error Handling." In Beginning Rust, 223–37. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3468-6_17.

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Hammar, Karima, Tounsia Djamah, and Maamar Bettayeb. "Fractional nonlinear output error system identification." In 2017 Proceedings of the Conference on Control and its Applications, 40–45. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611975024.6.

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Seok, Kyung Ha, and Daehyeon Cho. "Some Comments on Error Correcting Output Codes." In Fuzzy Systems and Knowledge Discovery, 383–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_44.

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Escalera, Sergio, Oriol Pujol, and Petia Radeva. "Decoding of Ternary Error Correcting Output Codes." In Lecture Notes in Computer Science, 753–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11892755_78.

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Zheng, Fa, Hui Xue, Xiaohong Chen, and Yunyun Wang. "Maximum Margin Tree Error Correcting Output Codes." In PRICAI 2016: Trends in Artificial Intelligence, 681–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42911-3_57.

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Ricci, Francesco, and David W. Aha. "Error-correcting output codes for local learners." In Machine Learning: ECML-98, 280–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026698.

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Simeone, Paolo, Claudio Marrocco, and Francesco Tortorella. "Shaping the Error-Reject Curve of Error Correcting Output Coding Systems." In Image Analysis and Processing – ICIAP 2011, 118–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24085-0_13.

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Conference papers on the topic "Output error"

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Boutat, D., G. Zheng, J. P. Barbot, and H. Hammouri. "Observer Error Linearization Multi-Output Depending." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.376918.

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Ding, Kaihua, and Krzysztof Fidkowski. "Output Error Control Using r-Adaptation." In 23rd AIAA Computational Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-4111.

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Shimizu, Yukiko S., and Krzysztof Fidkowski. "Output Error Estimation for Chaotic Flows." In 46th AIAA Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3806.

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Yamaguchi, N., and N. Ishii. "Constructing error correcting output coding classifiers." In 9th International Conference on Neural Information Processing. IEEE, 2002. http://dx.doi.org/10.1109/iconip.2002.1201973.

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Milne, Garth. "Interpreting error sources in output error parameter estimation with colored noise." In AIAA Atmospheric Flight Mechanics Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-4199.

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Grauer, Jared A. "Real-Time Parameter Estimation using Output Error." In AIAA Atmospheric Flight Mechanics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-2556.

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Bagheri, Mohammad Ali, Qigang Gao, and Sergio Escalera. "Rough Set Subspace Error-Correcting Output Codes." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.124.

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Pimenta, Edgar, and Joao Gama. "A study on Error Correcting Output Codes." In 2005 Purtuguese Conference on Artificial Intelligence. IEEE, 2005. http://dx.doi.org/10.1109/epia.2005.341298.

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Escalera, Sergio, Oriol Pujol, and Petia Radeva. "Separability of ternary Error-Correcting Output Codes." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761048.

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Zhang, Hao, Joey Zhou, Tianying Wang, Ivor Tsang, and Rick Siow Mong Goh. "Deep N-ary Error Correcting Output Codes." In Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace. EAI, 2020. http://dx.doi.org/10.4108/eai.27-8-2020.2299197.

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Reports on the topic "Output error"

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Wettergren, Thomas A. Stochastic Error Modeling of Beamformer Output for Arrays with Directive Elements. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada390305.

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Dutta, Soumya, Jonathan Lee Woodring, and James Paul Ahrens. Uncertainty, sensitivity, and error analysis and visualization of high-dimensional Input-output models. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1392895.

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Lichtenberg, Frank, and Zvi Griliches. Errors of Measurement in Output Deflators. Cambridge, MA: National Bureau of Economic Research, August 1986. http://dx.doi.org/10.3386/w2000.

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Siegel, Donald. Errors in Output Deflators Revisited: Unit Values and the PPI. Cambridge, MA: National Bureau of Economic Research, December 1991. http://dx.doi.org/10.3386/w3935.

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Ruosteenoja, Kimmo. Applicability of CMIP6 models for building climate projections for northern Europe. Finnish Meteorological Institute, September 2021. http://dx.doi.org/10.35614/isbn.9789523361416.

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In this report, we have evaluated the performance of nearly 40 global climate models (GCMs) participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). The focus is on the northern European area, but the ability to simulate southern European and global climate is discussed as well. Model evaluation was started with a technical control; completely unrealistic values in the GCM output files were identified by seeking the absolute minimum and maximum values. In this stage, one GCM was rejected totally, and furthermore individual output files from two other GCMs. In evaluating the remaining GCMs, the primary tool was the Model Climate Performance Index (MCPI) that combines RMS errors calculated for the different climate variables into one index. The index takes into account both the seasonal and spatial variations in climatological means. Here, MCPI was calculated for the period 1981—2010 by comparing GCM output with the ERA-Interim reanalyses. Climate variables explored in the evaluation were the surface air temperature, precipitation, sea level air pressure and incoming solar radiation at the surface. Besides MCPI, we studied RMS errors in the seasonal course of the spatial means by examining each climate variable separately. Furthermore, the evaluation procedure considered model performance in simulating past trends in the global-mean temperature, the compatibility of future responses to different greenhouse-gas scenarios and the number of available scenario runs. Daily minimum and maximum temperatures were likewise explored in a qualitative sense, but owing to the non-existence of data from multiple GCMs, these variables were not incorporated in the quantitative validation. Four of the 37 GCMs that had passed the initial technical check were regarded as wholly unusable for scenario calculations: in two GCMs the responses to the different greenhouse gas scenarios were contradictory and in two other GCMs data were missing from one of the four key climate variables. Moreover, to reduce inter-GCM dependencies, no more than two variants of any individual GCM were included; this led to an abandonment of one GCM. The remaining 32 GCMs were divided into three quality classes according to the assessed performance. The users of model data can utilize this grading to select a subset of GCMs to be used in elaborating climate projections for Finland or adjacent areas. Annual-mean temperature and precipitation projections for Finland proved to be nearly identical regardless of whether they were derived from the entire ensemble or by ignoring models that had obtained the lowest scores. Solar radiation projections were somewhat more sensitive.
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