Literatura académica sobre el tema "Kernel linear model"
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Artículos de revistas sobre el tema "Kernel linear model"
ASEERVATHAM, SUJEEVAN. "A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS". International Journal on Artificial Intelligence Tools 18, n.º 02 (abril de 2009): 239–72. http://dx.doi.org/10.1142/s0218213009000123.
Texto completoDIOŞAN, LAURA, ALEXANDRINA ROGOZAN y JEAN-PIERRE PECUCHET. "LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING". International Journal on Artificial Intelligence Tools 19, n.º 05 (octubre de 2010): 647–77. http://dx.doi.org/10.1142/s0218213010000352.
Texto completoSegera, Davies, Mwangi Mbuthia y Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis". BioMed Research International 2019 (16 de diciembre de 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.
Texto completoNehra, Rahul y 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, n.º 11 (30 de noviembre de 2023): 198–203. http://dx.doi.org/10.22214/ijraset.2023.56481.
Texto completoAndrade-Girón, Daniel, Edgardo Carreño-Cisneros, Cecilia Mejía-Dominguez, Julia Velásquez-Gamarra, William Marín-Rodriguez, Henry Villarreal-Torres y 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 de junio de 2023): e8. http://dx.doi.org/10.4108/eetpht.9.3472.
Texto completoCaraka, Rezzy Eko, Hasbi Yasin y Adi Waridi Basyiruddin. "Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis". Jurnal Matematika 7, n.º 1 (10 de junio de 2017): 43. http://dx.doi.org/10.24843/jmat.2017.v07.i01.p81.
Texto completoIVAN, KOMANG CANDRA, I. WAYAN SUMARJAYA y MADE SUSILAWATI. "ANALISIS MODEL REGRESI NONPARAMETRIK SIRKULAR-LINEAR BERGANDA". E-Jurnal Matematika 5, n.º 2 (31 de mayo de 2016): 52. http://dx.doi.org/10.24843/mtk.2016.v05.i02.p121.
Texto completoSunitha, Lingam y M. Bal Raju. "Multi-class classification for large datasets with optimized SVM by non-linear kernel function". Journal of Physics: Conference Series 2089, n.º 1 (1 de noviembre de 2021): 012015. http://dx.doi.org/10.1088/1742-6596/2089/1/012015.
Texto completoJan, A. R. "An Asymptotic Model for Solving Mixed Integral Equation in Position and Time". Journal of Mathematics 2022 (30 de agosto de 2022): 1–11. http://dx.doi.org/10.1155/2022/8063971.
Texto completoLumbanraja, Favorisen Rossyking, Reza Aji Saputra, Kurnia Muludi, Astria Hijriani y Akmal Junaidi. "IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG". Jurnal Pepadun 2, n.º 3 (1 de diciembre de 2021): 327–35. http://dx.doi.org/10.23960/pepadun.v2i3.90.
Texto completoTesis sobre el tema "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.
Texto completoHu, Zonghui. "Semiparametric functional data analysis for longitudinal/clustered data: theory and application". Texas A&M University, 2004. http://hdl.handle.net/1969.1/3088.
Texto completoKayhan, 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.
Texto completoone 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.
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
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/.
Texto completoSong, 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.
Texto completoIn 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/.
Texto completoVlachos, 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.
Texto completoFan, Liangdong. "ESTIMATION IN PARTIALLY LINEAR MODELS WITH CORRELATED OBSERVATIONS AND CHANGE-POINT MODELS". UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/32.
Texto completoZhai, Jing. "Efficient Exact Tests in Linear Mixed Models for Longitudinal Microbiome Studies". Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612412.
Texto completoLibros sobre el tema "Kernel linear model"
Xiang, Xiaojing. Asymptotic theory for linear functions of ordered observations. 1992.
Buscar texto completoChance, Kelly y Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.
Texto completoCapítulos de libros sobre el tema "Kernel linear model"
Lee, John, Jow-Ran Chang, Lie-Jane Kao y Cheng-Few Lee. "Kernel Linear Model". En 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.
Texto completoZhang, Yuehua, Peng Zhang y Yong Shi. "Kernel Based Regularized Multiple Criteria Linear Programming Model". En 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.
Texto completoFateh, Rachid, Anouar Darif y Said Safi. "Identification of the Linear Dynamic Parts of Wiener Model Using Kernel and Linear Adaptive". En 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.
Texto completoWong, Leon, Zhu-Hong You, Yu-An Huang, Xi Zhou y Mei-Yuan Cao. "A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions". En Intelligent Computing Theories and Application, 316–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60802-6_28.
Texto completoYamanishi, Yoshihiro. "Linear and Kernel Model Construction Methods for Predicting Drug–Target Interactions in a Chemogenomic Framework". En 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.
Texto completoSerjam, Chanakya y Akito Sakurai. "Analyzing Performance of High Frequency Currency Rates Prediction Model Using Linear Kernel SVR on Historical Data". En Intelligent Information and Database Systems, 498–507. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54472-4_47.
Texto completoDhankhar, Amita y Kamna Solanki. "Predicting Student’s Performance Using Linear Kernel Principal Component Analysis and Recurrent Neural Network (LKPCA-RNN) Model". En Proceedings of Data Analytics and Management, 637–46. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6285-0_51.
Texto completoPillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao y Lennart Ljung. "Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification". En Regularized System Identification, 247–311. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_7.
Texto completoLotufo, Rafael, Steven She, Thorsten Berger, Krzysztof Czarnecki y Andrzej Wąsowski. "Evolution of the Linux Kernel Variability Model". En 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.
Texto completoKirchner, Rosane M., Reinaldo C. Souza y Flávio A. Ziegelmann. "Identification of the Structure of Linear and Non-Linear Time Series Models, Using Nonparametric Local Linear Kernel Estimation". En 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.
Texto completoActas de conferencias sobre el tema "Kernel linear model"
De Luca, Patrick Medeiros y Wemerson Delcio Parreira. "Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels". En Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p004-006.
Texto completoFang, Yudong, Zhenfei Zhan, Junqi Yang, Jun Lu y Chong Chen. "A Mixed-Kernel-Based Support Vector Regression Model for Automotive Body Design Optimization". En ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67669.
Texto completoPillonetto, Gianluigi, Tianshi Chen y Lennart Ljung. "Kernel-based model order selection for identification and prediction of linear dynamic systems". En 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760702.
Texto completoAlSaihati, Ahmed, Salaheldin Elkatatny, Hani Gamal y Abdulazeez Abdulraheem. "A Statistical Machine Learning Model to Predict Equivalent Circulation Density ECD while Drilling, Based on Principal Components Analysis PCA". En SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/202101-ms.
Texto completoWang, Yi, Nan Xue, Xin Fan, Jiebo Luo, Risheng Liu, Bin Chen, Haojie Li y Zhongxuan Luo. "Fast Factorization-free Kernel Learning for Unlabeled Chunk Data Streams". 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.2018/393.
Texto completoJeffries, Brien, J. Wesley Hines, Albert Klein, Thomas Palmé y Romain Bayère. "Early Detection of Boiler Leakage in a Combined Cycle Power Plant Using an Auto Associative Kernel Regression Model". En ASME Turbo Expo 2013: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94216.
Texto completoOmran, Ashraf y Brett Newman. "Analytical Response for the Prototypic Nonlinear Mass-Spring-Damper System". En ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-24153.
Texto completoChen, Changyuan, Manases Tello Ruiz, Evert Lataire, Guillaume Delefortrie, Marc Mansuy, Tianlong Mei y Marc Vantorre. "Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm". En 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.
Texto completoHe, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He y Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel". En 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.
Texto completoIppili, Rajani K., Richard D. Widdle, Patricia Davies y Anil K. Bajaj. "Modeling and Identification of Polyurethane Foam in Uniaxial Compression: Combined Elastic and Viscoelastic Response". En 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.
Texto completoInformes sobre el tema "Kernel linear model"
Manninen, Terhikki y 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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