Gotowa bibliografia na temat „Kernel testing”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Kernel testing”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Kernel testing"
Chen, Zhengpu, Carl Wassgren i Kingsly Ambrose. "A Review of Grain Kernel Damage: Mechanisms, Modeling, and Testing Procedures". Transactions of the ASABE 63, nr 2 (2020): 455–75. http://dx.doi.org/10.13031/trans.13643.
Pełny tekst źródłaWu, Michael C., Arnab Maity, Seunggeun Lee, Elizabeth M. Simmons, Quaker E. Harmon, Xinyi Lin, Stephanie M. Engel, Jeffrey J. Molldrem i Paul M. Armistead. "Kernel Machine SNP-Set Testing Under Multiple Candidate Kernels". Genetic Epidemiology 37, nr 3 (7.03.2013): 267–75. http://dx.doi.org/10.1002/gepi.21715.
Pełny tekst źródłaKiefer, Nicholas M., i Timothy J. Vogelsang. "HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE". Econometric Theory 18, nr 6 (24.09.2002): 1350–66. http://dx.doi.org/10.1017/s026646660218604x.
Pełny tekst źródłaAhmad, Ibrahim, i A. R. Mugdadi. "Testing normality using kernel methods". Journal of Nonparametric Statistics 15, nr 3 (czerwiec 2003): 273–88. http://dx.doi.org/10.1080/1048525021000049649.
Pełny tekst źródłaMartinez, Kara, Arnab Maity, Robert H. Yolken, Patrick F. Sullivan i Jung‐Ying Tzeng. "Robust kernel association testing (RobKAT)". Genetic Epidemiology 44, nr 3 (14.01.2020): 272–82. http://dx.doi.org/10.1002/gepi.22280.
Pełny tekst źródłaTiaraSari, Arum, i Emy Haryatmi. "Penerapan Convolutional Neural Network Deep Learning dalam Pendeteksian Citra Biji Jagung Kering". Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, nr 2 (28.04.2021): 265–71. http://dx.doi.org/10.29207/resti.v5i2.3040.
Pełny tekst źródłaBruggink, H., H. L. Kraak, M. H. G. E. Dijkema i J. Bekendam. "Some factors influencing electrolyte leakage from maize (Zea mays L.) kernels". Seed Science Research 1, nr 1 (marzec 1991): 15–20. http://dx.doi.org/10.1017/s0960258500000581.
Pełny tekst źródłaHidayatullah, Martin Sulung, Tamrin Tamrin, Oktafri Oktafri i Warji Warji. "Rancang Bangun dan Uji Kinerja Alat Pemisah Kernel Sawit dari Cangkangnya dengan Menggunakan Larutan Garam". Jurnal Agricultural Biosystem Engineering 2, nr 2 (22.06.2023): 281. http://dx.doi.org/10.23960/jabe.v2i2.7482.
Pełny tekst źródłaPan, Shuang, Jianguo Wei i Hao Pan. "Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine". Scientific Programming 2020 (8.05.2020): 1–7. http://dx.doi.org/10.1155/2020/4561834.
Pełny tekst źródłaGao, Jiti, i Irène Gijbels. "Bandwidth Selection in Nonparametric Kernel Testing". Journal of the American Statistical Association 103, nr 484 (grudzień 2008): 1584–94. http://dx.doi.org/10.1198/016214508000000968.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaOzier-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.
Pełny tekst źródłaSingle-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.
Pełny tekst źródłaWe 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/.
Pełny tekst źródłaFriedrichs, Stefanie Verfasser], Heike [Akademischer Betreuer] Bickeböller, Thomas [Gutachter] [Kneib i 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.
Pełny tekst źródłaLi, Yinglei. "Genetic Association Testing of Copy Number Variation". UKnowledge, 2014. http://uknowledge.uky.edu/statistics_etds/8.
Pełny tekst źródłaAkcin, Haci Mustafa. "NONPARAMETRIC INFERENCES FOR THE HAZARD FUNCTION WITH RIGHT TRUNCATION". Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/math_diss/12.
Pełny tekst źródłaLi, Na. "MMD and Ward criterion in a RKHS : application to Kernel based hierarchical agglomerative clustering". Thesis, Troyes, 2015. http://www.theses.fr/2015TROY0033/document.
Pełny tekst źródłaClustering, 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.
Pełny tekst źródłaSingh, 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.
Pełny tekst źródłaKsiążki na temat "Kernel testing"
Nandakumar, Ratna. Kernel-smoothed DIF detection procedure for computerized adaptive tests. Newtown, PA: Law School Admission Council, 2006.
Znajdź pełny tekst źródłaMathew, John M. A three dimensional finite element model of a wheat kernel with layered material properties. 1992.
Znajdź pełny tekst źródłaCai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Redaktorzy Frédéric Ferraty i Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.
Pełny tekst źródłaCzęści książek na temat "Kernel testing"
Hirukawa, Masayuki. "Specification Testing". W Asymmetric Kernel Smoothing, 73–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5466-2_5.
Pełny tekst źródłaGarn, Bernhard, Fabian Würfl i Dimitris E. Simos. "KERIS: A CT Tool of the Linux Kernel with Dynamic Memory Analysis Capabilities". W 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.
Pełny tekst źródłaGheorghe, Marian, Rodica Ceterchi, Florentin Ipate i Savas Konur. "Kernel P Systems Modelling, Testing and Verification - Sorting Case Study". W Membrane Computing, 233–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54072-6_15.
Pełny tekst źródłaGheorghe, Marian, Florentin Ipate, Raluca Lefticaru i Ana Turlea. "Testing Identifiable Kernel P Systems Using an X-Machine Approach". W Membrane Computing, 142–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12797-8_11.
Pełny tekst źródłaRijmen, Frank, Yanxuan Qu i Alina A. Von Davier. "Hypothesis Testing of Equating Differences in the Kernel Equating Framework". W 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.
Pełny tekst źródłaDrebes, Roberto Jung, Gabriela Jacques-Silva, Joana Matos Fonseca da Trindade i Taisy Silva Weber. "A Kernel-Based Communication Fault Injector for Dependability Testing of Distributed Systems". W Lecture Notes in Computer Science, 177–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678779_13.
Pełny tekst źródłaAkcam, Halil, i Volker Lohweg. "Pollen Classification Based on Binary 2D Projections of Pollen Grains". W 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.
Pełny tekst źródłaKamil, Firmanilah, i Nely Kurnila. "Preliminary Testing of Coarse Aggregate, Fine Aggregate, and Palm Kernel Shell Waste Characteristics in Sustainable Construction". W 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.
Pełny tekst źródłaDubat, A. "Whole-Kernel Mixolab Testing for Different Cereals". W Mixolab, 85–88. Elsevier, 2013. http://dx.doi.org/10.1016/b978-1-891127-77-9.50016-9.
Pełny tekst źródłaSchmitt, Marcelo, i Paulo Meirelles. "Trusting Critical Open Source Components". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Kernel testing"
Kriege, Nils M., Christopher Morris, Anja Rey i Christian Sohler. "A Property Testing Framework for the Theoretical Expressivity of Graph Kernels". W 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.
Pełny tekst źródłaPatrick, Matthew, i Yue Jia. "Kernel Density Adaptive Random Testing". W 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2015. http://dx.doi.org/10.1109/icstw.2015.7107451.
Pełny tekst źródłaSun, Zhongchang, i Shaofeng Zou. "Robust Hypothesis Testing with Kernel Uncertainty Sets". W 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022. http://dx.doi.org/10.1109/isit50566.2022.9834349.
Pełny tekst źródłaChen, Yu, Fengguang Wu, Kuanlong Yu, Lei Zhang, Yuheng Chen, Yang Yang i Junjie Mao. "Instant Bug Testing Service for Linux Kernel". W 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.
Pełny tekst źródłaPambudi, Afief Dias, Michael Fauss i Abdelhak M. Zoubir. "Kernel-based cooperative robust sequential hypothesis testing". W 2018 International Conference on Signals and Systems (ICSigSys). IEEE, 2018. http://dx.doi.org/10.1109/icsigsys.2018.8373565.
Pełny tekst źródłaNikeshin, Alexei Viacheslavovich, i Victor Zinovievich Shnitman. "Testing the OpenvSwitch module of the Linux kernel network subsystem". W 25th Scientific Conference “Scientific Services & Internet – 2023”. Keldysh Institute of Applied Mathematics, 2023. http://dx.doi.org/10.20948/abrau-2023-4.
Pełny tekst źródłaPawlak, Miroslaw. "Signal model specification testing via kernel reconstruction methods". W 2015 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2015. http://dx.doi.org/10.1109/sampta.2015.7148939.
Pełny tekst źródłaNi, Tao, Zhongxu Yin, Qiang Wei i Qingxian Wang. "High-Coverage Security Testing for Windows Kernel Drivers". W 2012 4th International Conference on Multimedia Information Networking and Security (MINES). IEEE, 2012. http://dx.doi.org/10.1109/mines.2012.117.
Pełny tekst źródłaGrixti, S., N. Sammut, M. Hernek, E. Carrascosa, M. Masmano i A. Crespo. "Separation Kernel Robustness Testing: The XtratuM Case Study". W 2016 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2016. http://dx.doi.org/10.1109/cluster.2016.91.
Pełny tekst źródłaWu, Jiagu, Huajun Feng, Zhihai Xu, Qi Li i Zhongliang Fu. "Method to detect and calculate motion blur kernel". W 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, redaktorzy Yudong Zhang, José Sasián, Libin Xiang i Sandy To. SPIE, 2010. http://dx.doi.org/10.1117/12.866645.
Pełny tekst źródłaRaporty organizacyjne na temat "Kernel testing"
Sparks, Paul, Jesse Sherburn, William Heard i Brett Williams. Penetration modeling of ultra‐high performance concrete using multiscale meshfree methods. Engineer Research and Development Center (U.S.), wrzesień 2021. http://dx.doi.org/10.21079/11681/41963.
Pełny tekst źródłaMcMurray, J. W., C. M. Silva, G. W. Helmreich, T. J. Gerczak, J. A. Dyer, J. L. Collins, R. D. Hunt, T. B. Lindemer i K. A. Terrani. Production of Low Enriched Uranium Nitride Kernels for TRISO Particle Irradiation Testing. Office of Scientific and Technical Information (OSTI), czerwiec 2016. http://dx.doi.org/10.2172/1376320.
Pełny tekst źródłaBhattacharya, Sumit, Rachel Seibert, Andrew Nelson, Heather Connaway i 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), czerwiec 2021. http://dx.doi.org/10.2172/1807683.
Pełny tekst źródłaLynk, John. PR-610-163756-WEB Material Strength Verification. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), kwiecień 2019. http://dx.doi.org/10.55274/r0011573.
Pełny tekst źródła