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