Добірка наукової літератури з теми "Kernel Inference"
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Статті в журналах з теми "Kernel Inference":
Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (January 2, 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.
Rogers, Mark F., Colin Campbell, and Yiming Ying. "Probabilistic Inference of Biological Networks via Data Integration." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/707453.
LUGO-MARTINEZ, JOSE, and PREDRAG RADIVOJAC. "Generalized graphlet kernels for probabilistic inference in sparse graphs." Network Science 2, no. 2 (August 2014): 254–76. http://dx.doi.org/10.1017/nws.2014.14.
Lazarus, Eben, Daniel J. Lewis, and James H. Stock. "The Size‐Power Tradeoff in HAR Inference." Econometrica 89, no. 5 (2021): 2497–516. http://dx.doi.org/10.3982/ecta15404.
Billio, M. "Kernel-Based Indirect Inference." Journal of Financial Econometrics 1, no. 3 (September 1, 2003): 297–326. http://dx.doi.org/10.1093/jjfinec/nbg014.
Zhang, Li Lyna, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, and Yunxin Liu. "nn-METER." GetMobile: Mobile Computing and Communications 25, no. 4 (March 30, 2022): 19–23. http://dx.doi.org/10.1145/3529706.3529712.
Robinson, P. M. "INFERENCE ON NONPARAMETRICALLY TRENDING TIME SERIES WITH FRACTIONAL ERRORS." Econometric Theory 25, no. 6 (December 2009): 1716–33. http://dx.doi.org/10.1017/s0266466609990302.
Yuan, Ao. "Semiparametric inference with kernel likelihood." Journal of Nonparametric Statistics 21, no. 2 (February 2009): 207–28. http://dx.doi.org/10.1080/10485250802553382.
Cheng, Yansong, and Surajit Ray. "Multivariate Modality Inference Using Gaussian Kernel." Open Journal of Statistics 04, no. 05 (2014): 419–34. http://dx.doi.org/10.4236/ojs.2014.45041.
Agbokou, Komi, and Yaogan Mensah. "INFERENCE ON THE REPRODUCING KERNEL HILBERT SPACES." Universal Journal of Mathematics and Mathematical Sciences 15 (October 10, 2021): 11–29. http://dx.doi.org/10.17654/2277141722002.
Дисертації з теми "Kernel Inference":
Fouchet, Arnaud. "Kernel methods for gene regulatory network inference." Thesis, Evry-Val d'Essonne, 2014. http://www.theses.fr/2014EVRY0058/document.
New technologies in molecular biology, in particular dna microarrays, have greatly increased the quantity of available data. in this context, methods from mathematics and computer science have been actively developed to extract information from large datasets. in particular, the problem of gene regulatory network inference has been tackled using many different mathematical and statistical models, from the most basic ones (correlation, boolean or linear models) to the most elaborate (regression trees, bayesian models with latent variables). despite their qualities when applied to similar problems, kernel methods have scarcely been used for gene network inference, because of their lack of interpretability. in this thesis, two approaches are developed to obtain interpretable kernel methods. firstly, from a theoretical point of view, some kernel methods are shown to consistently estimate a transition function and its partial derivatives from a learning dataset. these estimations of partial derivatives allow to better infer the gene regulatory network than previous methods on realistic gene regulatory networks. secondly, an interpretable kernel methods through multiple kernel learning is presented. this method, called lockni, provides state-of-the-art results on real and realistically simulated datasets
Chan, Karen Pui-Shan. "Kernel density estimation, Bayesian inference and random effects model." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/13350.
Araya, Valdivia Ernesto. "Kernel spectral learning and inference in random geometric graphs." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM020.
This thesis has two main objectives. The first is to investigate the concentration properties of random kernel matrices, which are central in the study of kernel methods. The second objective is to study statistical inference problems on random geometric graphs. Both objectives are connected by the graphon formalism, which allows to represent a graph by a kernel function. We briefly recall the basics of the graphon model in the first chapter. In chapter two, we present a set of accurate concentration inequalities for individual eigenvalues of the kernel matrix, where our main contribution is to obtain inequalities that scale with the eigenvalue in consideration, implying convergence rates that are faster than parametric and often exponential, which hitherto has only been establish under assumptions which are too restrictive for graph applications. We specialized our results to the case of dot products kernels, highlighting its relation with the random geometric graph model. In chapter three, we study the problem of latent distances estimation on random geometric graphs on the Euclidean sphere. We propose an efficient spectral algorithm that use the adjacency matrix to construct an estimator for the latent distances, and prove finite sample guaranties for the estimation error, establishing its convergence rate. In chapter four, we extend the method developed in the previous chapter to the case of random geometric graphs on the Euclidean ball, a model that despite its formal similarities with the spherical case it is more flexible for modelling purposes. In particular, we prove that for certain parameter choices its degree profile is power law distributed, which has been observed in many real life networks. All the theoretical findings of the last two chapters are verified and complemented by numerical experiments
Jitkrittum, Wittawat. "Kernel-based distribution features for statistical tests and Bayesian inference." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037987/.
Hsu, Yuan-Shuo Kelvin. "Bayesian Perspectives on Conditional Kernel Mean Embeddings: Hyperparameter Learning and Probabilistic Inference." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24309.
Adams, R. P. "Kernel methods for nonparametric Bayesian inference of probability densities and point processes." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595350.
Gogolashvili, Davit. "Global and local Kernel methods for dataset shift, scalable inference and optimization." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS363v2.pdf.
In many real world problems, the training data and test data have different distributions. The most common settings for dataset shift often considered in the literature are covariate shift and target shift. In this thesis, we investigate nonparametric models applied to the dataset shift scenario. We develop a novel framework to accelerate Gaussian process regression. In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. We propose a new method for estimating the minimizer and the minimum value of a smooth and strongly convex regression function from the observations contaminated by random noise
Maity, Arnab. "Efficient inference in general semiparametric regression models." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3075.
Minnier, Jessica. "Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10327.
Weller, Jennifer N. "Bayesian Inference In Forecasting Volcanic Hazards: An Example From Armenia." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000485.
Книги з теми "Kernel Inference":
Fauzi, Rizky Reza, and Yoshihiko Maesono. Statistical Inference Based on Kernel Distribution Function Estimators. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1.
Silva, Catarina. Inductive inference for large scale text classification: Kernel approaches and techniques. Berlin: Springer, 2010.
Wand, M. P. Kernel smoothing. London: Chapman & Hall, 1995.
Causal Inference from Statistical Data. Berlin, Germany: Logos-Verlag Berlin, 2008.
Silva, Catarina, and Bernadete Ribeiro. Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques. Springer, 2012.
Jones, M. C., and M. P. Wand. Kernel Smoothing. Taylor & Francis Group, 1994.
Jones, M. C., and M. P. Wand. Kernel Smoothing. Taylor & Francis Group, 1994.
Частини книг з теми "Kernel Inference":
Vovk, Vladimir. "Kernel Ridge Regression." In Empirical Inference, 105–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6_11.
Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel Quantile Estimation." In Statistical Inference Based on Kernel Distribution Function Estimators, 29–44. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_3.
Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel Density Function Estimator." In Statistical Inference Based on Kernel Distribution Function Estimators, 1–16. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_1.
Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel Distribution Function Estimator." In Statistical Inference Based on Kernel Distribution Function Estimators, 17–28. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_2.
Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel-Based Nonparametric Tests." In Statistical Inference Based on Kernel Distribution Function Estimators, 67–96. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_5.
Silva, Catarina, and Bernardete Ribeiro. "Kernel Machines for Text Classification." In Inductive Inference for Large Scale Text Classification, 31–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-04533-2_2.
Vert, Jean-Philippe. "Classification of Biological Sequences with Kernel Methods." In Grammatical Inference: Algorithms and Applications, 7–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11872436_2.
Christmann, Andreas, and Robert Hable. "On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods." In Empirical Inference, 231–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6_20.
Fukumizu, Kenji. "Nonparametric Bayesian Inference with Kernel Mean Embedding." In Modern Methodology and Applications in Spatial-Temporal Modeling, 1–24. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55339-7_1.
Fauzi, Rizky Reza, and Yoshihiko Maesono. "Mean Residual Life Estimator." In Statistical Inference Based on Kernel Distribution Function Estimators, 45–65. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_4.
Тези доповідей конференцій з теми "Kernel Inference":
Krajsek, Kai, and Hanno Scharr. "Bayesian inference in kernel feature space." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2012. http://dx.doi.org/10.1063/1.3703633.
Sigal, L., R. Memisevic, and D. J. Fleet. "Shared Kernel Information Embedding for discriminative inference." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206576.
Castro, Ivan, Cristobal Silva, and Felipe Tobar. "Initialising kernel adaptive filters via probabilistic inference." In 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096055.
Sigal, Leonid, Roland Memisevic, and David J. Fleet. "Shared Kernel Information Embedding for discriminative inference." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206576.
Doherty, Kevin, Jinkun Wang, and Brendan Englot. "Bayesian generalized kernel inference for occupancy map prediction." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989356.
Moschitti, Alessandro, Daniele Pighin, and Roberto Basili. "Semantic role labeling via tree kernel joint inference." In the Tenth Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1596276.1596289.
Seki, Hirosato, Fuhito Mizuguchi, Satoshi Watanabe, Hiroaki Ishii, and Masaharu Mizumoto. "SIRMs connected fuzzy inference method using kernel method." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811546.
Kekatos, Vassilis, Yu Zhang, and Georgios B. Giannakis. "Low-rank kernel learning for electricity market inference." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810605.
Perez-Suay, Adrian, and Gustau Camps-Valls. "Causal inference in geosciences with kernel sensitivity maps." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127064.
Guevara, Jorge, Jerry M. Mendel, and R. Hirata. "Connections Between Fuzzy Inference Systems and Kernel Machines." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177604.