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Artykuły w czasopismach na temat "Approximation de Nyström"
Ding, Lizhong, Yong Liu, Shizhong Liao, Yu Li, Peng Yang, Yijie Pan, Chao Huang, Ling Shao i Xin Gao. "Approximate Kernel Selection with Strong Approximate Consistency". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 3462–69. http://dx.doi.org/10.1609/aaai.v33i01.33013462.
Pełny tekst źródłaWang, Ling, Hongqiao Wang i Guangyuan Fu. "Multi-Nyström Method Based on Multiple Kernel Learning for Large Scale Imbalanced Classification". Computational Intelligence and Neuroscience 2021 (13.06.2021): 1–11. http://dx.doi.org/10.1155/2021/9911871.
Pełny tekst źródłaZhang, Kai, i James T. Kwok. "Density-Weighted Nyström Method for Computing Large Kernel Eigensystems". Neural Computation 21, nr 1 (styczeń 2009): 121–46. http://dx.doi.org/10.1162/neco.2009.11-07-651.
Pełny tekst źródłaDíaz de Alba, Patricia, Luisa Fermo i Giuseppe Rodriguez. "Solution of second kind Fredholm integral equations by means of Gauss and anti-Gauss quadrature rules". Numerische Mathematik 146, nr 4 (18.11.2020): 699–728. http://dx.doi.org/10.1007/s00211-020-01163-7.
Pełny tekst źródłaRudi, Alessandro, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil i Simone Severini. "Approximating Hamiltonian dynamics with the Nyström method". Quantum 4 (20.02.2020): 234. http://dx.doi.org/10.22331/q-2020-02-20-234.
Pełny tekst źródłaTrokicić, Aleksandar, i Branimir Todorović. "Constrained spectral clustering via multi–layer graph embeddings on a grassmann manifold". International Journal of Applied Mathematics and Computer Science 29, nr 1 (1.03.2019): 125–37. http://dx.doi.org/10.2478/amcs-2019-0010.
Pełny tekst źródłaCai, Difeng, i Panayot S. Vassilevski. "Eigenvalue Problems for Exponential-Type Kernels". Computational Methods in Applied Mathematics 20, nr 1 (1.01.2020): 61–78. http://dx.doi.org/10.1515/cmam-2018-0186.
Pełny tekst źródłaHe, Li, i Hong Zhang. "Kernel K-Means Sampling for Nyström Approximation". IEEE Transactions on Image Processing 27, nr 5 (maj 2018): 2108–20. http://dx.doi.org/10.1109/tip.2018.2796860.
Pełny tekst źródłaWang, Shiyuan, Lujuan Dang, Guobing Qian i Yunxiang Jiang. "Kernel recursive maximum correntropy with Nyström approximation". Neurocomputing 329 (luty 2019): 424–32. http://dx.doi.org/10.1016/j.neucom.2018.10.064.
Pełny tekst źródłaLaguardia, Anna Lucia, i Maria Grazia Russo. "A Nyström Method for 2D Linear Fredholm Integral Equations on Curvilinear Domains". Mathematics 11, nr 23 (3.12.2023): 4859. http://dx.doi.org/10.3390/math11234859.
Pełny tekst źródłaRozprawy doktorskie na temat "Approximation de Nyström"
Cherfaoui, Farah. "Echantillonnage pour l'accélération des méthodes à noyaux et sélection gloutonne pour les représentations parcimonieuses". Electronic Thesis or Diss., Aix-Marseille, 2022. http://www.theses.fr/2022AIXM0256.
Pełny tekst źródłaThe contributions of this thesis are divided into two parts. The first part is dedicated to the acceleration of kernel methods and the second to optimization under sparsity constraints. Kernel methods are widely known and used in machine learning. However, the complexity of their implementation is high and they become unusable when the number of data is large. We first propose an approximation of Ridge leverage scores. We then use these scores to define a probability distribution for the sampling process of the Nyström method in order to speed up the kernel methods. We then propose a new kernel-based framework for representing and comparing discrete probability distributions. We then exploit the link between our framework and the maximum mean discrepancy to propose an accurate and fast approximation of the latter. The second part of this thesis is devoted to optimization with sparsity constraint for signal optimization and random forest pruning. First, we prove under certain conditions on the coherence of the dictionary, the reconstruction and convergence properties of the Frank-Wolfe algorithm. Then, we use the OMP algorithm to reduce the size of random forests and thus reduce the size needed for its storage. The pruned forest consists of a subset of trees from the initial forest selected and weighted by OMP in order to minimize its empirical prediction error
Li, Jun 1977. "A computational model for the diffusion coefficients of DNA with applications". Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-05-1098.
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Części książek na temat "Approximation de Nyström"
Hutchings, Matthew, i Bertrand Gauthier. "Local Optimisation of Nyström Samples Through Stochastic Gradient Descent". W Machine Learning, Optimization, and Data Science, 123–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25599-1_10.
Pełny tekst źródłaFu, Zhouyu. "Optimal Landmark Selection for Nyström Approximation". W Neural Information Processing, 311–18. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_38.
Pełny tekst źródłaLi, Hongyu, i Lin Zhang. "Dynamic Subspace Update with Incremental Nyström Approximation". W Computer Vision – ACCV 2010 Workshops, 384–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22819-3_39.
Pełny tekst źródłaZhang, Huaxiang, Zhichao Wang i Linlin Cao. "Fast Nyström for Low Rank Matrix Approximation". W Advanced Data Mining and Applications, 456–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35527-1_38.
Pełny tekst źródłaFrammartino, Carmelina. "A Nyström Method for Solving a Boundary Value Problem on [0, ∞)". W Approximation and Computation, 311–25. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6594-3_20.
Pełny tekst źródłaJia, Hongjie, Liangjun Wang i Heping Song. "Large-Scale Spectral Clustering with Stochastic Nyström Approximation". W IFIP Advances in Information and Communication Technology, 26–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46931-3_3.
Pełny tekst źródłaAllouch, Chafik, Ikram Hamzaoui i Driss Sbibih. "Richardson Extrapolation of Nyström Method Associated with a Sextic Spline Quasi-Interpolant". W Mathematical and Computational Methods for Modelling, Approximation and Simulation, 105–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94339-4_5.
Pełny tekst źródłaYun, Jeong-Min, i Seungjin Choi. "Nyström Approximations for Scalable Face Recognition: A Comparative Study". W Neural Information Processing, 325–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_38.
Pełny tekst źródłaStreszczenia konferencji na temat "Approximation de Nyström"
Giffon, Luc, Stephane Ayache, Thierry Artieres i Hachem Kadri. "Deep Networks with Adaptive Nyström Approximation". W 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851711.
Pełny tekst źródłaZhang, Kai, Ivor W. Tsang i James T. Kwok. "Improved Nyström low-rank approximation and error analysis". W the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390311.
Pełny tekst źródłaMathur, Anant, Sarat Moka i Zdravko Botev. "Column Subset Selection and Nyström Approximation via Continuous Optimization". W 2023 Winter Simulation Conference (WSC). IEEE, 2023. http://dx.doi.org/10.1109/wsc60868.2023.10407416.
Pełny tekst źródłaMünch, Maximilian, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif i Thomas Villmann. "Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection". W ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2023. http://dx.doi.org/10.14428/esann/2023.es2023-136.
Pełny tekst źródłaPatel, Raajen, Tom Goldstein, Eva Dyer, Azalia Mirhoseini i Richard Baraniuk. "Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition". W Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2016. http://dx.doi.org/10.1137/1.9781611974348.67.
Pełny tekst źródłaLee, Jieun, i Yoonsik Choe. "Graph-Regularized Fast Low-Rank Matrix Approximation Using The NystrÖM Method for Clustering". W 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018. http://dx.doi.org/10.1109/mlsp.2018.8517034.
Pełny tekst źródłaDereziński, Michał, Rajiv Khanna i Michael W. Mahoney. "Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract)". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/647.
Pełny tekst źródłaAhmed, Hesham Ibrahim, Wan Qun, Ding Xue-ke i Zhou Zhi-ping. "Squared distance matrix completion through Nystrom approximation". W 2016 22nd Asia-Pacific Conference on Communications (APCC). IEEE, 2016. http://dx.doi.org/10.1109/apcc.2016.7581449.
Pełny tekst źródłaHou, Bo-Jian, Lijun Zhang i Zhi-Hua Zhou. "Storage Fit Learning with Unlabeled Data". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/256.
Pełny tekst źródłaPatel, Lokendra Singh, Suman Sana i S. P. Ghrera. "Efficient Nystrom method for low rank approximation and error analysis". W 2015 Third International Conference on Image Information Processing (ICIIP). IEEE, 2015. http://dx.doi.org/10.1109/iciip.2015.7414831.
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