Literatura académica sobre el tema "Kernel testing"
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Artículos de revistas sobre el tema "Kernel testing"
Chen, Zhengpu, Carl Wassgren y Kingsly Ambrose. "A Review of Grain Kernel Damage: Mechanisms, Modeling, and Testing Procedures". Transactions of the ASABE 63, n.º 2 (2020): 455–75. http://dx.doi.org/10.13031/trans.13643.
Texto completoWu, Michael C., Arnab Maity, Seunggeun Lee, Elizabeth M. Simmons, Quaker E. Harmon, Xinyi Lin, Stephanie M. Engel, Jeffrey J. Molldrem y Paul M. Armistead. "Kernel Machine SNP-Set Testing Under Multiple Candidate Kernels". Genetic Epidemiology 37, n.º 3 (7 de marzo de 2013): 267–75. http://dx.doi.org/10.1002/gepi.21715.
Texto completoKiefer, Nicholas M. y Timothy J. Vogelsang. "HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE". Econometric Theory 18, n.º 6 (24 de septiembre de 2002): 1350–66. http://dx.doi.org/10.1017/s026646660218604x.
Texto completoAhmad, Ibrahim y A. R. Mugdadi. "Testing normality using kernel methods". Journal of Nonparametric Statistics 15, n.º 3 (junio de 2003): 273–88. http://dx.doi.org/10.1080/1048525021000049649.
Texto completoMartinez, Kara, Arnab Maity, Robert H. Yolken, Patrick F. Sullivan y Jung‐Ying Tzeng. "Robust kernel association testing (RobKAT)". Genetic Epidemiology 44, n.º 3 (14 de enero de 2020): 272–82. http://dx.doi.org/10.1002/gepi.22280.
Texto completoTiaraSari, Arum y Emy Haryatmi. "Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, n.º 2 (28 de abril de 2021): 265–71. http://dx.doi.org/10.29207/resti.v5i2.3040.
Texto completoBruggink, H., H. L. Kraak, M. H. G. E. Dijkema y J. Bekendam. "Some factors influencing electrolyte leakage from maize (Zea mays L.) kernels". Seed Science Research 1, n.º 1 (marzo de 1991): 15–20. http://dx.doi.org/10.1017/s0960258500000581.
Texto completoHidayatullah, Martin Sulung, Tamrin Tamrin, Oktafri Oktafri y Warji Warji. "Rancang Bangun dan Uji Kinerja Alat Pemisah Kernel Sawit dari Cangkangnya dengan Menggunakan Larutan Garam". Jurnal Agricultural Biosystem Engineering 2, n.º 2 (22 de junio de 2023): 281. http://dx.doi.org/10.23960/jabe.v2i2.7482.
Texto completoPan, Shuang, Jianguo Wei y Hao Pan. "Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine". Scientific Programming 2020 (8 de mayo de 2020): 1–7. http://dx.doi.org/10.1155/2020/4561834.
Texto completoGao, Jiti y Irène Gijbels. "Bandwidth Selection in Nonparametric Kernel Testing". Journal of the American Statistical Association 103, n.º 484 (diciembre de 2008): 1584–94. http://dx.doi.org/10.1198/016214508000000968.
Texto completoTesis sobre el tema "Kernel testing"
Lee, Kevin Sung-ho. "Kernel-adaptor interface testing of Project Timeliner". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/49939.
Texto completoOzier-Lafontaine, Anthony. "Kernel-based testing and their application to single-cell data". Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0025.
Texto completoSingle-cell technologies generate data at the single-cell level. They are coumposed of hundreds to thousands of observations (i.e. cells) and tens of thousands of variables (i.e. genes). New methodological challenges arose to fully exploit the potentialities of these complex data. A major statistical challenge is to distinguish biological informationfrom technical noise in order to compare conditions or tissues. This thesis explores the application of kernel testing on single-cell datasets in order to detect and describe the potential differences between compared conditions.To overcome the limitations of existing kernel two-sample tests, we propose a kernel test inspired from the Hotelling-Lawley test that can apply to any experimental design. We implemented these tests in a R and Python package called ktest that is their first useroriented implementation. We demonstrate the performances of kernel testing on simulateddatasets and on various experimental singlecell datasets. The geometrical interpretations of these methods allows to identify the observations leading a detected difference. Finally, we propose a Nyström-based efficient implementationof these kernel tests as well as a range of diagnostic and interpretation tools
Kotlyarova, Yulia. "Kernel estimators : testing and bandwidth selection in models of unknown smoothness". Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85179.
Texto completoWe present theoretical results on the asymptotic distribution of the estimators under various smoothness assumptions and derive the limiting joint distributions for estimators with different combinations of bandwidths and kernel functions. Using these nontrivial joint distributions, we suggest a new way of improving accuracy and robustness of the estimators by considering a linear combination of estimators with different smoothing parameters. The weights in the combination minimize an estimate of the mean squared error. Monte Carlo simulations confirm suitability of this method for both smooth and non-smooth models.
For the original and smoothed maximum score estimators, a formal procedure is introduced to test for equivalence of the maximum likelihood estimators and these semiparametric estimators, which converge to the true value at slower rates. The test allows one to identify heteroskedastic misspecifications in the logit/probit models. The method has been applied to analyze the decision of married women to join the labour force.
Liero, Hannelore. "Testing the Hazard Rate, Part I". Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5151/.
Texto completoFriedrichs, Stefanie Verfasser], Heike [Akademischer Betreuer] Bickeböller, Thomas [Gutachter] [Kneib y Tim [Gutachter] Beißbarth. "Kernel-Based Pathway Approaches for Testing and Selection / Stefanie Friedrichs ; Gutachter: Thomas Kneib, Tim Beißbarth ; Betreuer: Heike Bickeböller". Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2017. http://d-nb.info/114137952X/34.
Texto completoLi, Yinglei. "Genetic Association Testing of Copy Number Variation". UKnowledge, 2014. http://uknowledge.uky.edu/statistics_etds/8.
Texto completoAkcin, Haci Mustafa. "NONPARAMETRIC INFERENCES FOR THE HAZARD FUNCTION WITH RIGHT TRUNCATION". Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/math_diss/12.
Texto completoLi, Na. "MMD and Ward criterion in a RKHS : application to Kernel based hierarchical agglomerative clustering". Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0033/document.
Texto completoClustering, as a useful tool for unsupervised classification, is the task of grouping objects according to some measured or perceived characteristics of them and it has owned great success in exploring the hidden structure of unlabeled data sets. Kernel-based clustering algorithms have shown great prominence. They provide competitive performance compared with conventional methods owing to their ability of transforming nonlinear problem into linear ones in a higher dimensional feature space. In this work, we propose a Kernel-based Hierarchical Agglomerative Clustering algorithms (KHAC) using Ward’s criterion. Our method is induced by a recently arisen criterion called Maximum Mean Discrepancy (MMD). This criterion has firstly been proposed to measure difference between different distributions and can easily be embedded into a RKHS. Close relationships have been proved between MMD and Ward's criterion. In our KHAC method, selection of the kernel parameter and determination of the number of clusters have been studied, which provide satisfactory performance. Finally an iterative KHAC algorithm is proposed which aims at determining the optimal kernel parameter, giving a meaningful number of clusters and partitioning the data set automatically
Bissyande, Tegawende. "Contributions for improving debugging of kernel-level services in a monolithic operating system". Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00821893.
Texto completoSingh, Yuvraj. "Regression Models to Predict Coastdown Road Load for Various Vehicle Types". The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595265184541326.
Texto completoLibros sobre el tema "Kernel testing"
Nandakumar, Ratna. Kernel-smoothed DIF detection procedure for computerized adaptive tests. Newtown, PA: Law School Admission Council, 2006.
Buscar texto completoMathew, John M. A three dimensional finite element model of a wheat kernel with layered material properties. 1992.
Buscar texto completoCai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Editado por Frédéric Ferraty y Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.
Texto completoCapítulos de libros sobre el tema "Kernel testing"
Hirukawa, Masayuki. "Specification Testing". En Asymmetric Kernel Smoothing, 73–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5466-2_5.
Texto completoGarn, Bernhard, Fabian Würfl y Dimitris E. Simos. "KERIS: A CT Tool of the Linux Kernel with Dynamic Memory Analysis Capabilities". En Hardware and Software: Verification and Testing, 225–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70389-3_17.
Texto completoGheorghe, Marian, Rodica Ceterchi, Florentin Ipate y Savas Konur. "Kernel P Systems Modelling, Testing and Verification - Sorting Case Study". En Membrane Computing, 233–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54072-6_15.
Texto completoGheorghe, Marian, Florentin Ipate, Raluca Lefticaru y Ana Turlea. "Testing Identifiable Kernel P Systems Using an X-Machine Approach". En Membrane Computing, 142–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12797-8_11.
Texto completoRijmen, Frank, Yanxuan Qu y Alina A. Von Davier. "Hypothesis Testing of Equating Differences in the Kernel Equating Framework". En Statistical Models for Test Equating, Scaling, and Linking, 317–26. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98138-3_19.
Texto completoDrebes, Roberto Jung, Gabriela Jacques-Silva, Joana Matos Fonseca da Trindade y Taisy Silva Weber. "A Kernel-Based Communication Fault Injector for Dependability Testing of Distributed Systems". En Lecture Notes in Computer Science, 177–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678779_13.
Texto completoAkcam, Halil y Volker Lohweg. "Pollen Classification Based on Binary 2D Projections of Pollen Grains". En Technologien für die intelligente Automation, 273–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64283-2_20.
Texto completoKamil, Firmanilah y Nely Kurnila. "Preliminary Testing of Coarse Aggregate, Fine Aggregate, and Palm Kernel Shell Waste Characteristics in Sustainable Construction". En Proceedings of the International Conference on Applied Science and Technology on Social Science 2023 (iCAST-SS 2023), 614–20. Paris: Atlantis Press SARL, 2023. http://dx.doi.org/10.2991/978-2-38476-202-6_88.
Texto completoDubat, A. "Whole-Kernel Mixolab Testing for Different Cereals". En Mixolab, 85–88. Elsevier, 2013. http://dx.doi.org/10.1016/b978-1-891127-77-9.50016-9.
Texto completoSchmitt, Marcelo y Paulo Meirelles. "Trusting Critical Open Source Components". En Business Models and Strategies for Open Source Projects, 175–99. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-4785-7.ch006.
Texto completoActas de conferencias sobre el tema "Kernel testing"
Kriege, Nils M., Christopher Morris, Anja Rey y Christian Sohler. "A Property Testing Framework for the Theoretical Expressivity of Graph Kernels". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/325.
Texto completoPatrick, Matthew y Yue Jia. "Kernel Density Adaptive Random Testing". En 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2015. http://dx.doi.org/10.1109/icstw.2015.7107451.
Texto completoSun, Zhongchang y Shaofeng Zou. "Robust Hypothesis Testing with Kernel Uncertainty Sets". En 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022. http://dx.doi.org/10.1109/isit50566.2022.9834349.
Texto completoChen, Yu, Fengguang Wu, Kuanlong Yu, Lei Zhang, Yuheng Chen, Yang Yang y Junjie Mao. "Instant Bug Testing Service for Linux Kernel". En 2013 IEEE International Conference on High Performance Computing and Communications (HPCC) & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2013. http://dx.doi.org/10.1109/hpcc.and.euc.2013.347.
Texto completoPambudi, Afief Dias, Michael Fauss y Abdelhak M. Zoubir. "Kernel-based cooperative robust sequential hypothesis testing". En 2018 International Conference on Signals and Systems (ICSigSys). IEEE, 2018. http://dx.doi.org/10.1109/icsigsys.2018.8373565.
Texto completoNikeshin, Alexei Viacheslavovich y Victor Zinovievich Shnitman. "Testing the OpenvSwitch module of the Linux kernel network subsystem". En 25th Scientific Conference “Scientific Services & Internet – 2023”. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/abrau-2023-4.
Texto completoPawlak, Miroslaw. "Signal model specification testing via kernel reconstruction methods". En 2015 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2015. http://dx.doi.org/10.1109/sampta.2015.7148939.
Texto completoNi, Tao, Zhongxu Yin, Qiang Wei y Qingxian Wang. "High-Coverage Security Testing for Windows Kernel Drivers". En 2012 4th International Conference on Multimedia Information Networking and Security (MINES). IEEE, 2012. http://dx.doi.org/10.1109/mines.2012.117.
Texto completoGrixti, S., N. Sammut, M. Hernek, E. Carrascosa, M. Masmano y A. Crespo. "Separation Kernel Robustness Testing: The XtratuM Case Study". En 2016 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2016. http://dx.doi.org/10.1109/cluster.2016.91.
Texto completoWu, Jiagu, Huajun Feng, Zhihai Xu, Qi Li y Zhongliang Fu. "Method to detect and calculate motion blur kernel". En 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, editado por Yudong Zhang, José Sasián, Libin Xiang y Sandy To. SPIE, 2010. http://dx.doi.org/10.1117/12.866645.
Texto completoInformes sobre el tema "Kernel testing"
Sparks, Paul, Jesse Sherburn, William Heard y Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), septiembre de 2021. http://dx.doi.org/10.21079/11681/41963.
Texto completoMcMurray, J. W., C. M. Silva, G. W. Helmreich, T. J. Gerczak, J. A. Dyer, J. L. Collins, R. D. Hunt, T. B. Lindemer y K. A. Terrani. Production of Low Enriched Uranium Nitride Kernels for TRISO Particle Irradiation Testing. Office of Scientific and Technical Information (OSTI), junio de 2016. http://dx.doi.org/10.2172/1376320.
Texto completoBhattacharya, Sumit, Rachel Seibert, Andrew Nelson, Heather Connaway y Abdellatif Yacout. Preliminary results from Low Pressure Steam Oxidation Testing of ALD ZrN and ZrO2 Coating Deposited over UCN Fuel Kernels. Office of Scientific and Technical Information (OSTI), junio de 2021. http://dx.doi.org/10.2172/1807683.
Texto completoLynk, John. PR-610-163756-WEB Material Strength Verification. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), abril de 2019. http://dx.doi.org/10.55274/r0011573.
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