Artículos de revistas sobre el tema "Variable sparsity kernel learning"
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Chen, Jingxiang, Chong Zhang, Michael R. Kosorok y Yufeng Liu. "Double sparsity kernel learning with automatic variable selection and data extraction". Statistics and Its Interface 11, n.º 3 (2018): 401–20. http://dx.doi.org/10.4310/sii.2018.v11.n3.a1.
Texto completoHuang, Yuan y Shuangge Ma. "Discussion on “Double sparsity kernel learning with automatic variable selection and data extraction”". Statistics and Its Interface 11, n.º 3 (2018): 421–22. http://dx.doi.org/10.4310/sii.2018.v11.n3.a2.
Texto completoLiu, Meimei y Guang Cheng. "Discussion on “Double sparsity kernel learning with automatic variable selection and data extraction”". Statistics and Its Interface 11, n.º 3 (2018): 423–24. http://dx.doi.org/10.4310/sii.2018.v11.n3.a3.
Texto completoZhang, Hao Helen. "Discussion on “Doubly sparsity kernel learning with automatic variable selection and data extraction”". Statistics and Its Interface 11, n.º 3 (2018): 425–28. http://dx.doi.org/10.4310/sii.2018.v11.n3.a4.
Texto completoChen, Jingxiang, Chong Zhang, Michael R. Kosorok y Yufeng Liu. "Rejoinder of “Double sparsity kernel learning with automatic variable selection and data extraction”". Statistics and Its Interface 11, n.º 3 (2018): 429–31. http://dx.doi.org/10.4310/sii.2018.v11.n3.a5.
Texto completoWang, Shuangyue y Ziyan Luo. "Sparse Support Tensor Machine with Scaled Kernel Functions". Mathematics 11, n.º 13 (24 de junio de 2023): 2829. http://dx.doi.org/10.3390/math11132829.
Texto completoPan, Chao, Cheng Shi, Honglang Mu, Jie Li y Xinbo Gao. "EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands". Applied Sciences 10, n.º 5 (29 de febrero de 2020): 1619. http://dx.doi.org/10.3390/app10051619.
Texto completoKoltchinskii, Vladimir y Ming Yuan. "Sparsity in multiple kernel learning". Annals of Statistics 38, n.º 6 (diciembre de 2010): 3660–95. http://dx.doi.org/10.1214/10-aos825.
Texto completoJiang, Zhengxiong, Yingsong Li, Xinqi Huang y Zhan Jin. "A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems". Symmetry 11, n.º 10 (1 de octubre de 2019): 1218. http://dx.doi.org/10.3390/sym11101218.
Texto completoYuan, Ying, Weiming Lu, Fei Wu y Yueting Zhuang. "Multiple kernel learning with NOn-conVex group spArsity". Journal of Visual Communication and Image Representation 25, n.º 7 (octubre de 2014): 1616–24. http://dx.doi.org/10.1016/j.jvcir.2014.08.001.
Texto completoZhang, Lijun, Rong Jin, Chun Chen, Jiajun Bu y Xiaofei He. "Efficient Online Learning for Large-Scale Sparse Kernel Logistic Regression". Proceedings of the AAAI Conference on Artificial Intelligence 26, n.º 1 (20 de septiembre de 2021): 1219–25. http://dx.doi.org/10.1609/aaai.v26i1.8300.
Texto completoPeng, Jialin, Xiaofeng Zhu, Ye Wang, Le An y Dinggang Shen. "Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis". Pattern Recognition 88 (abril de 2019): 370–82. http://dx.doi.org/10.1016/j.patcog.2018.11.027.
Texto completoSuzuki, Taiji y Masashi Sugiyama. "Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness". Annals of Statistics 41, n.º 3 (junio de 2013): 1381–405. http://dx.doi.org/10.1214/13-aos1095.
Texto completoYu, Han, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu y Xingxuan Zhang. "Stable Learning via Sparse Variable Independence". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 10998–1006. http://dx.doi.org/10.1609/aaai.v37i9.26303.
Texto completoKumar, Kuldeep, Kaleem Siddiqi y Christian Desrosiers. "White matter fiber analysis using kernel dictionary learning and sparsity priors". Pattern Recognition 95 (noviembre de 2019): 83–95. http://dx.doi.org/10.1016/j.patcog.2019.06.002.
Texto completoTong, Anh, Toan M. Tran, Hung Bui y Jaesik Choi. "Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de mayo de 2021): 9906–14. http://dx.doi.org/10.1609/aaai.v35i11.17190.
Texto completoNiu, Wei, Mengshu Sun, Zhengang Li, Jou-An Chen, Jiexiong Guan, Xipeng Shen, Yanzhi Wang, Sijia Liu, Xue Lin y Bin Ren. "RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 9179–87. http://dx.doi.org/10.1609/aaai.v35i10.17108.
Texto completoBabeetha, S., B. Muruganantham, S. Ganesh Kumar y A. Murugan. "An enhanced kernel weighted collaborative recommended system to alleviate sparsity". International Journal of Electrical and Computer Engineering (IJECE) 10, n.º 1 (1 de febrero de 2020): 447. http://dx.doi.org/10.11591/ijece.v10i1.pp447-454.
Texto completoGe, Ting, Tianming Zhan, Qinfeng Li y Shanxiang Mu. "Optimal Superpixel Kernel-Based Kernel Low-Rank and Sparsity Representation for Brain Tumour Segmentation". Computational Intelligence and Neuroscience 2022 (24 de junio de 2022): 1–12. http://dx.doi.org/10.1155/2022/3514988.
Texto completoLin, Shaobo, Jinshan Zeng, Jian Fang y Zongben Xu. "Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel". Neural Computation 26, n.º 10 (octubre de 2014): 2350–78. http://dx.doi.org/10.1162/neco_a_00641.
Texto completoMatsui, Kota, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi y Takafumi Kanamori. "Variable Selection for Nonparametric Learning with Power Series Kernels". Neural Computation 31, n.º 8 (agosto de 2019): 1718–50. http://dx.doi.org/10.1162/neco_a_01212.
Texto completoWilkinson, Lucas, Kazem Cheshmi y Maryam Mehri Dehnavi. "Register Tiling for Unstructured Sparsity in Neural Network Inference". Proceedings of the ACM on Programming Languages 7, PLDI (6 de junio de 2023): 1995–2020. http://dx.doi.org/10.1145/3591302.
Texto completoSun, Yiheng, Tian Lu, Cong Wang, Yuan Li, Huaiyu Fu, Jingran Dong y Yunjie Xu. "TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 12181–90. http://dx.doi.org/10.1609/aaai.v36i11.21478.
Texto completoXia, Yifan, Yongchao Hou y Shaogao Lv. "Learning rates for partially linear support vector machine in high dimensions". Analysis and Applications 19, n.º 01 (28 de octubre de 2020): 167–82. http://dx.doi.org/10.1142/s0219530520400126.
Texto completoMa, Wenlu y Han Liu. "Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning". Information Technology and Control 50, n.º 2 (17 de junio de 2021): 319–31. http://dx.doi.org/10.5755/j01.itc.50.2.27752.
Texto completoGu, Yanfeng, Guoming Gao, Deshan Zuo y Di You. "Model Selection and Classification With Multiple Kernel Learning for Hyperspectral Images via Sparsity". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, n.º 6 (junio de 2014): 2119–30. http://dx.doi.org/10.1109/jstars.2014.2318181.
Texto completoLiu, Chang, Lixin Tang y Jiyin Liu. "Least squares support vector machine with self-organizing multiple kernel learning and sparsity". Neurocomputing 331 (febrero de 2019): 493–504. http://dx.doi.org/10.1016/j.neucom.2018.11.067.
Texto completoDong, Xue-Mei, Hao Weng, Jian Shi y Yinhe Gu. "Randomized multi-scale kernels learning with sparsity constraint regularization for regression". International Journal of Wavelets, Multiresolution and Information Processing 17, n.º 06 (noviembre de 2019): 1950048. http://dx.doi.org/10.1142/s0219691319500486.
Texto completoStephens, Hunter, Q. Jackie Wu y Qiuwen Wu. "Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization". Biomedical Physics & Engineering Express 8, n.º 1 (12 de noviembre de 2021): 017001. http://dx.doi.org/10.1088/2057-1976/ac35f8.
Texto completoJing, Yongjun, Hao Wang, Kun Shao, Xing Huo y Yangyang Zhang. "Unsupervised Graph Representation Learning With Variable Heat Kernel". IEEE Access 8 (2020): 15800–15811. http://dx.doi.org/10.1109/access.2020.2966409.
Texto completoLowe, David G. "Similarity Metric Learning for a Variable-Kernel Classifier". Neural Computation 7, n.º 1 (enero de 1995): 72–85. http://dx.doi.org/10.1162/neco.1995.7.1.72.
Texto completoBoßelmann, Christian Malte, Ulrike B. S. Hedrich, Holger Lerche y Nico Pfeifer. "Predicting functional effects of ion channel variants using new phenotypic machine learning methods". PLOS Computational Biology 19, n.º 3 (6 de marzo de 2023): e1010959. http://dx.doi.org/10.1371/journal.pcbi.1010959.
Texto completoXie, Zhonghua, Lingjun Liu y Cui Yang. "An Entropy-Based Algorithm with Nonlocal Residual Learning for Image Compressive Sensing Recovery". Entropy 21, n.º 9 (17 de septiembre de 2019): 900. http://dx.doi.org/10.3390/e21090900.
Texto completoTao, Zhou, Chang XiaoYu, Lu HuiLing, Ye XinYu, Liu YunCan y Zheng XiaoMin. "Pooling Operations in Deep Learning: From “Invariable” to “Variable”". BioMed Research International 2022 (20 de junio de 2022): 1–17. http://dx.doi.org/10.1155/2022/4067581.
Texto completoZhao, Ji, Hongbin Zhang y Xiaofeng Liao. "Variable learning rates kernel adaptive filter with single feedback". Digital Signal Processing 83 (diciembre de 2018): 59–72. http://dx.doi.org/10.1016/j.dsp.2018.06.007.
Texto completoWang, Yanbo, Quan Liu y Bo Yuan. "Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity". Computational and Mathematical Methods in Medicine 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/2078214.
Texto completoFattahi, Loubna El y El Hassan Sbai. "Clustering using kernel entropy principal component analysis and variable kernel estimator". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 3 (1 de junio de 2021): 2109. http://dx.doi.org/10.11591/ijece.v11i3.pp2109-2119.
Texto completoHuang, Shimeng, Elisabeth Ailer, Niki Kilbertus y Niklas Pfister. "Supervised learning and model analysis with compositional data". PLOS Computational Biology 19, n.º 6 (30 de junio de 2023): e1011240. http://dx.doi.org/10.1371/journal.pcbi.1011240.
Texto completoSCHLEIF, F. M., THOMAS VILLMANN, BARBARA HAMMER y PETRA SCHNEIDER. "EFFICIENT KERNELIZED PROTOTYPE BASED CLASSIFICATION". International Journal of Neural Systems 21, n.º 06 (diciembre de 2011): 443–57. http://dx.doi.org/10.1142/s012906571100295x.
Texto completoLiu, Zhou-zhou y Shi-ning Li. "WSNs Compressed Sensing Signal Reconstruction Based on Improved Kernel Fuzzy Clustering and Discrete Differential Evolution Algorithm". Journal of Sensors 2019 (16 de junio de 2019): 1–9. http://dx.doi.org/10.1155/2019/7039510.
Texto completoJi, Xiaojia, Xuanyi Lu, Chunhong Guo, Weiwei Pei y Hui Xu. "Predictions of Geological Interface Using Relevant Vector Machine with Borehole Data". Sustainability 14, n.º 16 (15 de agosto de 2022): 10122. http://dx.doi.org/10.3390/su141610122.
Texto completoChristmann, Andreas y Ding-Xuan Zhou. "Learning rates for the risk of kernel-based quantile regression estimators in additive models". Analysis and Applications 14, n.º 03 (13 de abril de 2016): 449–77. http://dx.doi.org/10.1142/s0219530515500050.
Texto completoPrasad, Srijanani Anurag. "Reproducing Kernel Hilbert Space and Coalescence Hidden-variable Fractal Interpolation Functions". Demonstratio Mathematica 52, n.º 1 (30 de septiembre de 2019): 467–74. http://dx.doi.org/10.1515/dema-2019-0027.
Texto completoCui, Lipeng, Jie Shen y Song Yao. "The Sparse Learning of The Support Vector Machine". Journal of Physics: Conference Series 2078, n.º 1 (1 de noviembre de 2021): 012006. http://dx.doi.org/10.1088/1742-6596/2078/1/012006.
Texto completoWang, Fan y Guige Gao. "Optimization of short-term wind power prediction of Multi-kernel Extreme Learning Machine based on Sparrow Search Algorithm". Journal of Physics: Conference Series 2527, n.º 1 (1 de junio de 2023): 012075. http://dx.doi.org/10.1088/1742-6596/2527/1/012075.
Texto completoZhu, Shuang, Xiangang Luo, Zhanya Xu y Lei Ye. "Seasonal streamflow forecasts using mixture-kernel GPR and advanced methods of input variable selection". Hydrology Research 50, n.º 1 (8 de junio de 2018): 200–214. http://dx.doi.org/10.2166/nh.2018.023.
Texto completoNiu, Guo, Zhengming Ma y Shuyu Liu. "A Multikernel-Like Learning Algorithm Based on Data Probability Distribution". Mathematical Problems in Engineering 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/5927306.
Texto completoAl-Aamri, Amira, Kamal Taha, Maher Maalouf, Andrzej Kudlicki y Dirar Homouz. "Inferring Causation in Yeast Gene Association Networks With Kernel Logistic Regression". Evolutionary Bioinformatics 16 (enero de 2020): 117693432092031. http://dx.doi.org/10.1177/1176934320920310.
Texto completoWu, Xingyu, Bingbing Jiang, Tianhao Wu y Huanhuan Chen. "Practical Markov Boundary Learning without Strong Assumptions". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 9 (26 de junio de 2023): 10388–98. http://dx.doi.org/10.1609/aaai.v37i9.26236.
Texto completoLi, Meng-Yu, Rui-Qi Wang, Jian-Bo Zhang y Zhong-Ke Gao. "Characterizing gas–liquid two-phase flow behavior using complex network and deep learning". Chaos: An Interdisciplinary Journal of Nonlinear Science 33, n.º 1 (enero de 2023): 013108. http://dx.doi.org/10.1063/5.0124998.
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