Literatura científica selecionada sobre o tema "Ridge leverage scores"
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Artigos de revistas sobre o assunto "Ridge leverage scores"
Pedde, Meredith, Adam Szpiro, Richard A. Hirth e Sara D. Adar. "School Bus Rebate Program and Student Educational Performance Test Scores". JAMA Network Open 7, n.º 3 (20 de março de 2024): e243121. http://dx.doi.org/10.1001/jamanetworkopen.2024.3121.
Texto completo da fonteVijayanand, Deepshika, e Subbulakshmi P. "Beyond the Grind: Leveraging Data Analysis and Machine Learning for the Quantification and Enhancement of Work-Life Balance". International Journal of Membrane Science and Technology 10, n.º 1 (11 de outubro de 2023): 718–34. http://dx.doi.org/10.15379/ijmst.v10i1.2634.
Texto completo da fonteGarcía-Portugués, Eduardo, e Arturo Prieto-Tirado. "Toroidal PCA via density ridges". Statistics and Computing 33, n.º 5 (24 de julho de 2023). http://dx.doi.org/10.1007/s11222-023-10273-9.
Texto completo da fonteTeses / dissertações sobre o assunto "Ridge leverage scores"
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.
Texto completo da fonteThe 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
Capítulos de livros sobre o assunto "Ridge leverage scores"
S, Srividya M., e Anala M. R. "Machine Learning Based Framework for Human Action Detection". In Data Science and Intelligent Computing Techniques, 849–57. Soft Computing Research Society, 2023. http://dx.doi.org/10.56155/978-81-955020-2-8-72.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Ridge leverage scores"
Cherfaoui, Farah, Hachem Kadri e Liva Ralaivola. "Scalable Ridge Leverage Score Sampling for the Nyström Method". In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747039.
Texto completo da fonteCohen, Michael B., Cameron Musco e Christopher Musco. "Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling". In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2017. http://dx.doi.org/10.1137/1.9781611974782.115.
Texto completo da fonte