Literatura académica sobre el tema "Variable sparsity kernel learning"
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Artículos de revistas sobre el tema "Variable sparsity kernel learning"
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 completoTesis sobre el tema "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.
Texto completoLe, 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.
Texto completoHakala, Tim. "Settling-Time Improvements in Positioning Machines Subject to Nonlinear Friction Using Adaptive Impulse Control". BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1061.
Texto completoSankaran, Raman. "Structured Regularization Through Convex Relaxations Of Discrete Penalties". Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5456.
Texto completoNaudé, Johannes Jochemus. "Aircraft recognition using generalised variable-kernel similarity metric learning". Thesis, 2014. http://hdl.handle.net/10210/13113.
Texto completoNearest 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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Capítulos de libros sobre el tema "Variable sparsity kernel learning"
Koltchinskii, Vladimir, Dmitry Panchenko y Savina Andonova. "Generalization Bounds for Voting Classifiers Based on Sparsity and Clustering". En 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.
Texto completoNaudé, Johannes J., Michaël A. van Wyk y Barend J. van Wyk. "Generalized Variable-Kernel Similarity Metric Learning". En 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.
Texto completoU. Torun, Mustafa, Onur Yilmaz y Ali N. Akansu. "Explicit Kernel and Sparsity of Eigen Subspace for the AR(1) Process". En Financial Signal Processing and Machine Learning, 67–99. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch5.
Texto completoGregorová, Magda, Jason Ramapuram, Alexandros Kalousis y Stéphane Marchand-Maillet. "Large-Scale Nonlinear Variable Selection via Kernel Random Features". En 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.
Texto completoConnolly, Andrew J., Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas y Alexander Gray. "Regression and Model Fitting". En Statistics, Data Mining, and Machine Learning in Astronomy. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691151687.003.0008.
Texto completoT., Subbulakshmi. "Combating Cyber Security Breaches in Digital World Using Misuse Detection Methods". En 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.
Texto completoWong, Andrew K. C., Yang Wang y Gary C. L. Li. "Pattern Discovery as Event Association". En Machine Learning, 1924–32. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch804.
Texto completoActas de conferencias sobre el tema "Variable sparsity kernel learning"
Dellacasagrande, Matteo, Davide Lengani, Pietro Paliotta, Daniele Petronio, Daniele Simoni y Francesco Bertini. "Evaluation of Different Regression Models Tuned With Experimental Turbine Cascade Data". En ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-81357.
Texto completoYokoi, Sho, Daichi Mochihashi, Ryo Takahashi, Naoaki Okazaki y Kentaro Inui. "Learning Co-Substructures by Kernel Dependence Maximization". En 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.
Texto completoVahdat, Arash, Kevin Cannons, Greg Mori, Sangmin Oh y Ilseo Kim. "Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach". En 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.463.
Texto completoHe, Jia, Changying Du, Changde Du, Fuzhen Zhuang, Qing He y Guoping Long. "Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel". En 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.
Texto completoGarcia-Vega, S., E. A. Leon-Gomez y G. Castellanos-Dominguez. "Time Series Prediction for Kernel-based Adaptive Filters Using Variable Bandwidth, Adaptive Learning-rate, and Dimensionality Reduction". En ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683117.
Texto completoSclavounos, Paul D. y Yu Ma. "Artificial Intelligence Machine Learning in Marine Hydrodynamics". En 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.
Texto completoHu, Chao, Gaurav Jain, Craig Schmidt, Carrie Strief y Melani Sullivan. "Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning". En 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.
Texto completoLiu, Yanchi, Tan Yan y Haifeng Chen. "Exploiting Graph Regularized Multi-dimensional Hawkes Processes for Modeling Events with Spatio-temporal Characteristics". En 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.
Texto completoCheng, Hongliang, Weilin Yi y Luchen Ji. "Multi-Point Optimization Design of High Pressure-Ratio Centrifugal Impeller Based on Machine Learning". En ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14576.
Texto completoReda Ali, Ahmed, Makky Sandra Jaya y Ernest A. Jones. "Machine Learning Strategies for Accurate Log Prediction in Reservoir Characterization: Self-Calibrating Versus Domain-Knowledge". En SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205602-ms.
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