Добірка наукової літератури з теми "Variable sparsity kernel learning"
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Статті в журналах з теми "Variable sparsity kernel learning"
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 джерелаДисертації з теми "Variable sparsity kernel learning"
Kolar, Mladen. "Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/229.
Повний текст джерелаLe, Van Luong. "Identification de systèmes dynamiques hybrides : géométrie, parcimonie et non-linéarités." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00874283.
Повний текст джерелаHakala, Tim. "Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1061.
Повний текст джерелаSankaran, Raman. "Structured Regularization Through Convex Relaxations Of Discrete Penalties." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5456.
Повний текст джерелаNaudé, Johannes Jochemus. "Aircraft recognition using generalised variable-kernel similarity metric learning." Thesis, 2014. http://hdl.handle.net/10210/13113.
Повний текст джерелаNearest neighbour classifiers are well suited for use in practical pattern recognition applications for a number of reasons, including ease of implementation, rapid training, justifiable decisions and low computational load. However their generalisation performance is perceived to be inferior to that of more complex methods such as neural networks or support vector machines. Closer inspection shows however that the generalisation performance actually varies widely depending on the dataset used. On certain problems they outperform all other known classifiers while on others they fail dismally. In this thesis we allege that their sensitivity to the metric used is the reason for their mercurial performance. We also discuss some of the remedies for this problem that have been suggested in the past, most notably the variable-kernel similarity metric learning technique, and introduce our own extension to this technique. Finally these metric learning techniques are evaluated on an aircraft recognition task and critically compared.
Hwang, Sung Ju. "Discriminative object categorization with external semantic knowledge." 2013. http://hdl.handle.net/2152/21320.
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Частини книг з теми "Variable sparsity kernel learning"
Koltchinskii, Vladimir, Dmitry Panchenko, and Savina Andonova. "Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering." In Learning Theory and Kernel Machines, 492–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_36.
Повний текст джерелаNaudé, Johannes J., Michaël A. van Wyk, and Barend J. van Wyk. "Generalized Variable-Kernel Similarity Metric Learning." In Lecture Notes in Computer Science, 788–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27868-9_86.
Повний текст джерелаU. Torun, Mustafa, Onur Yilmaz, and Ali N. Akansu. "Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process." In Financial Signal Processing and Machine Learning, 67–99. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch5.
Повний текст джерелаGregorová, Magda, Jason Ramapuram, Alexandros Kalousis, and Stéphane Marchand-Maillet. "Large-Scale Nonlinear Variable Selection via Kernel Random Features." In Machine Learning and Knowledge Discovery in Databases, 177–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10928-8_11.
Повний текст джерелаConnolly, Andrew J., Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray. "Regression and Model Fitting." In Statistics, Data Mining, and Machine Learning in Astronomy. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691151687.003.0008.
Повний текст джерелаT., Subbulakshmi. "Combating Cyber Security Breaches in Digital World Using Misuse Detection Methods." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 85–92. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0193-0.ch006.
Повний текст джерелаWong, Andrew K. C., Yang Wang, and Gary C. L. Li. "Pattern Discovery as Event Association." In Machine Learning, 1924–32. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch804.
Повний текст джерелаТези доповідей конференцій з теми "Variable sparsity kernel learning"
Dellacasagrande, Matteo, Davide Lengani, Pietro Paliotta, Daniele Petronio, Daniele Simoni, and Francesco Bertini. "Evaluation of Different Regression Models Tuned With Experimental Turbine Cascade Data." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-81357.
Повний текст джерелаYokoi, Sho, Daichi Mochihashi, Ryo Takahashi, Naoaki Okazaki, and Kentaro Inui. "Learning Co-Substructures by Kernel Dependence Maximization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/465.
Повний текст джерелаVahdat, Arash, Kevin Cannons, Greg Mori, Sangmin Oh, and Ilseo Kim. "Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.463.
Повний текст джерелаHe, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, and Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/254.
Повний текст джерелаGarcia-Vega, S., E. A. Leon-Gomez, and G. Castellanos-Dominguez. "Time Series Prediction for Kernel-based Adaptive Filters Using Variable Bandwidth, Adaptive Learning-rate, and Dimensionality Reduction." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683117.
Повний текст джерелаSclavounos, Paul D., and Yu Ma. "Artificial Intelligence Machine Learning in Marine Hydrodynamics." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77599.
Повний текст джерелаHu, Chao, Gaurav Jain, Craig Schmidt, Carrie Strief, and Melani Sullivan. "Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46964.
Повний текст джерелаLiu, Yanchi, Tan Yan, and Haifeng Chen. "Exploiting Graph Regularized Multi-dimensional Hawkes Processes for Modeling Events with Spatio-temporal Characteristics." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/343.
Повний текст джерелаCheng, Hongliang, Weilin Yi, and Luchen Ji. "Multi-Point Optimization Design of High Pressure-Ratio Centrifugal Impeller Based on Machine Learning." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14576.
Повний текст джерелаReda Ali, Ahmed, Makky Sandra Jaya, and Ernest A. Jones. "Machine Learning Strategies for Accurate Log Prediction in Reservoir Characterization: Self-Calibrating Versus Domain-Knowledge." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205602-ms.
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