Littérature scientifique sur le sujet « Kernel linear model »
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Articles de revues sur le sujet "Kernel linear model"
ASEERVATHAM, SUJEEVAN. « A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS ». International Journal on Artificial Intelligence Tools 18, no 02 (avril 2009) : 239–72. http://dx.doi.org/10.1142/s0218213009000123.
Texte intégralDIOŞAN, LAURA, ALEXANDRINA ROGOZAN et JEAN-PIERRE PECUCHET. « LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING ». International Journal on Artificial Intelligence Tools 19, no 05 (octobre 2010) : 647–77. http://dx.doi.org/10.1142/s0218213010000352.
Texte intégralSegera, Davies, Mwangi Mbuthia et Abraham Nyete. « Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis ». BioMed Research International 2019 (16 décembre 2019) : 1–11. http://dx.doi.org/10.1155/2019/4085725.
Texte intégralNehra, Rahul, et Kamalpreet Kaur. « AI-based Optimization of Tensile Strength of the Cement Concrete Incorporating Recycled Mixed Plastic Fine used in Road Construction ». International Journal for Research in Applied Science and Engineering Technology 11, no 11 (30 novembre 2023) : 198–203. http://dx.doi.org/10.22214/ijraset.2023.56481.
Texte intégralAndrade-Girón, Daniel, Edgardo Carreño-Cisneros, Cecilia Mejía-Dominguez, Julia Velásquez-Gamarra, William Marín-Rodriguez, Henry Villarreal-Torres et Rosana Meleán-Romero. « Support vector machine with optimized parameters for the classification of patients with COVID-19 ». EAI Endorsed Transactions on Pervasive Health and Technology 9 (20 juin 2023) : e8. http://dx.doi.org/10.4108/eetpht.9.3472.
Texte intégralCaraka, Rezzy Eko, Hasbi Yasin et Adi Waridi Basyiruddin. « Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis ». Jurnal Matematika 7, no 1 (10 juin 2017) : 43. http://dx.doi.org/10.24843/jmat.2017.v07.i01.p81.
Texte intégralIVAN, KOMANG CANDRA, I. WAYAN SUMARJAYA et MADE SUSILAWATI. « ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA ». E-Jurnal Matematika 5, no 2 (31 mai 2016) : 52. http://dx.doi.org/10.24843/mtk.2016.v05.i02.p121.
Texte intégralSunitha, Lingam, et M. Bal Raju. « Multi-class classification for large datasets with optimized SVM by non-linear kernel function ». Journal of Physics : Conference Series 2089, no 1 (1 novembre 2021) : 012015. http://dx.doi.org/10.1088/1742-6596/2089/1/012015.
Texte intégralJan, A. R. « An Asymptotic Model for Solving Mixed Integral Equation in Position and Time ». Journal of Mathematics 2022 (30 août 2022) : 1–11. http://dx.doi.org/10.1155/2022/8063971.
Texte intégralLumbanraja, Favorisen Rossyking, Reza Aji Saputra, Kurnia Muludi, Astria Hijriani et Akmal Junaidi. « IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG ». Jurnal Pepadun 2, no 3 (1 décembre 2021) : 327–35. http://dx.doi.org/10.23960/pepadun.v2i3.90.
Texte intégralThèses sur le sujet "Kernel linear model"
Roberts, Gareth James. « Monitoring land cover dynamics using linear kernel-driven BRDF model parameter temporal trajectories ». Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407145.
Texte intégralHu, Zonghui. « Semiparametric functional data analysis for longitudinal/clustered data : theory and application ». Texas A&M University, 2004. http://hdl.handle.net/1969.1/3088.
Texte intégralKayhan, Belgin. « Parameter Estimation In Generalized Partial Linear Modelswith Tikhanov Regularization ». Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612530/index.pdf.
Texte intégralone has interaction and the other one does not have. As well as studying the regularization of the nonparametric part, we also mention theoretically the regularization of the parametric part. Furthermore, we make a comparison between Infinite Kernel Learning (IKL) and Tikhonov regularization by using two data sets, with the difference consisting in the (non-)homogeneity of the data set. The thesis concludes with an outlook on future research.
Ozier-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.
Texte intégralSingle-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
Vassura, Edoardo. « Path integrals on curved space and the worldline formalism ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13448/.
Texte intégralSong, Song. « Confidence bands in quantile regression and generalized dynamic semiparametric factor models ». Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.
Texte intégralIn many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
Piccini, Jacopo. « Data Dependent Convergence Guarantees for Regression Problems in Neural Networks ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24218/.
Texte intégralVlachos, Dimitrios. « Novel algorithms in wireless CDMA systems for estimation and kernel based equalization ». Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7658.
Texte intégralFan, Liangdong. « ESTIMATION IN PARTIALLY LINEAR MODELS WITH CORRELATED OBSERVATIONS AND CHANGE-POINT MODELS ». UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/32.
Texte intégralZhai, Jing. « Efficient Exact Tests in Linear Mixed Models for Longitudinal Microbiome Studies ». Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612412.
Texte intégralLivres sur le sujet "Kernel linear model"
Xiang, Xiaojing. Asymptotic theory for linear functions of ordered observations. 1992.
Trouver le texte intégralChance, Kelly, et Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.
Texte intégralChapitres de livres sur le sujet "Kernel linear model"
Lee, John, Jow-Ran Chang, Lie-Jane Kao et Cheng-Few Lee. « Kernel Linear Model ». Dans Essentials of Excel VBA, Python, and R, 261–77. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-14283-3_12.
Texte intégralZhang, Yuehua, Peng Zhang et Yong Shi. « Kernel Based Regularized Multiple Criteria Linear Programming Model ». Dans Lecture Notes in Computer Science, 625–32. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01973-9_70.
Texte intégralFateh, Rachid, Anouar Darif et Said Safi. « Identification of the Linear Dynamic Parts of Wiener Model Using Kernel and Linear Adaptive ». Dans Advanced Intelligent Systems for Sustainable Development (AI2SD’2020), 387–400. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90639-9_31.
Texte intégralWong, Leon, Zhu-Hong You, Yu-An Huang, Xi Zhou et Mei-Yuan Cao. « A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions ». Dans Intelligent Computing Theories and Application, 316–25. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60802-6_28.
Texte intégralYamanishi, Yoshihiro. « Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework ». Dans Methods in Molecular Biology, 355–68. New York, NY : Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8639-2_12.
Texte intégralSerjam, Chanakya, et Akito Sakurai. « Analyzing Performance of High Frequency Currency Rates Prediction Model Using Linear Kernel SVR on Historical Data ». Dans Intelligent Information and Database Systems, 498–507. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54472-4_47.
Texte intégralDhankhar, Amita, et Kamna Solanki. « Predicting Student’s Performance Using Linear Kernel Principal Component Analysis and Recurrent Neural Network (LKPCA-RNN) Model ». Dans Proceedings of Data Analytics and Management, 637–46. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6285-0_51.
Texte intégralPillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao et Lennart Ljung. « Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification ». Dans Regularized System Identification, 247–311. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_7.
Texte intégralLotufo, Rafael, Steven She, Thorsten Berger, Krzysztof Czarnecki et Andrzej Wąsowski. « Evolution of the Linux Kernel Variability Model ». Dans Software Product Lines : Going Beyond, 136–50. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15579-6_10.
Texte intégralKirchner, Rosane M., Reinaldo C. Souza et Flávio A. Ziegelmann. « Identification of the Structure of Linear and Non-Linear Time Series Models, Using Nonparametric Local Linear Kernel Estimation ». Dans Soft Methodology and Random Information Systems, 589–96. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44465-7_73.
Texte intégralActes de conférences sur le sujet "Kernel linear model"
De Luca, Patrick Medeiros, et Wemerson Delcio Parreira. « Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels ». Dans Computer on the Beach. Itajaí : Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p004-006.
Texte intégralFang, Yudong, Zhenfei Zhan, Junqi Yang, Jun Lu et Chong Chen. « A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization ». Dans ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67669.
Texte intégralPillonetto, Gianluigi, Tianshi Chen et Lennart Ljung. « Kernel-based model order selection for identification and prediction of linear dynamic systems ». Dans 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760702.
Texte intégralAlSaihati, Ahmed, Salaheldin Elkatatny, Hani Gamal et Abdulazeez Abdulraheem. « A Statistical Machine Learning Model to Predict Equivalent Circulation Density ECD while Drilling, Based on Principal Components Analysis PCA ». Dans SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/202101-ms.
Texte intégralWang, Yi, Nan Xue, Xin Fan, Jiebo Luo, Risheng Liu, Bin Chen, Haojie Li et Zhongxuan Luo. « Fast Factorization-free Kernel Learning for Unlabeled Chunk Data Streams ». Dans 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.2018/393.
Texte intégralJeffries, Brien, J. Wesley Hines, Albert Klein, Thomas Palmé et Romain Bayère. « Early Detection of Boiler Leakage in a Combined Cycle Power Plant Using an Auto Associative Kernel Regression Model ». Dans ASME Turbo Expo 2013 : Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94216.
Texte intégralOmran, Ashraf, et Brett Newman. « Analytical Response for the Prototypic Nonlinear Mass-Spring-Damper System ». Dans ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24153.
Texte intégralChen, Changyuan, Manases Tello Ruiz, Evert Lataire, Guillaume Delefortrie, Marc Mansuy, Tianlong Mei et Marc Vantorre. « Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm ». Dans ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95565.
Texte intégralHe, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He et Guoping Long. « Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel ». Dans Twenty-Sixth International Joint Conference on Artificial Intelligence. California : International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/254.
Texte intégralIppili, Rajani K., Richard D. Widdle, Patricia Davies et Anil K. Bajaj. « Modeling and Identification of Polyurethane Foam in Uniaxial Compression : Combined Elastic and Viscoelastic Response ». Dans ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48485.
Texte intégralRapports d'organisations sur le sujet "Kernel linear model"
Manninen, Terhikki, et Pauline Stenberg. Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361379.
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