Academic literature on the topic 'Machine learning, kernel methods'
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Journal articles on the topic "Machine learning, kernel methods"
Hofmann, Thomas, Bernhard Schölkopf, and Alexander J. Smola. "Kernel methods in machine learning." Annals of Statistics 36, no. 3 (June 2008): 1171–220. http://dx.doi.org/10.1214/009053607000000677.
Full textSchaback, Robert, and Holger Wendland. "Kernel techniques: From machine learning to meshless methods." Acta Numerica 15 (May 2006): 543–639. http://dx.doi.org/10.1017/s0962492906270016.
Full textMengoni, Riccardo, and Alessandra Di Pierro. "Kernel methods in Quantum Machine Learning." Quantum Machine Intelligence 1, no. 3-4 (November 15, 2019): 65–71. http://dx.doi.org/10.1007/s42484-019-00007-4.
Full textZhang, Senyue, and Wenan Tan. "An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel." Discrete Dynamics in Nature and Society 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7293278.
Full textINOKUCHI, RYO, and SADAAKI MIYAMOTO. "KERNEL METHODS FOR CLUSTERING: COMPETITIVE LEARNING AND c-MEANS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 04 (August 2006): 481–93. http://dx.doi.org/10.1142/s0218488506004138.
Full textChristmann, Andreas, Florian Dumpert, and Dao-Hong Xiang. "On extension theorems and their connection to universal consistency in machine learning." Analysis and Applications 14, no. 06 (October 25, 2016): 795–808. http://dx.doi.org/10.1142/s0219530516400029.
Full textSaxena, Arti, and Vijay Kumar. "Bayesian Kernel Methods." International Journal of Big Data and Analytics in Healthcare 6, no. 1 (January 2021): 26–39. http://dx.doi.org/10.4018/ijbdah.20210101.oa3.
Full textVidnerová, Petra, and Roman Neruda. "Air Pollution Modelling by Machine Learning Methods." Modelling 2, no. 4 (November 17, 2021): 659–74. http://dx.doi.org/10.3390/modelling2040035.
Full textRahmati, Marzie, and Mohammad Ali Zare Chahooki. "Improvement in bug localization based on kernel extreme learning machine." Journal of Communications Technology, Electronics and Computer Science 5 (April 30, 2016): 1. http://dx.doi.org/10.22385/jctecs.v5i0.77.
Full textPrice, Stanton R., Derek T. Anderson, Timothy C. Havens, and Steven R. Price. "Kernel Matrix-Based Heuristic Multiple Kernel Learning." Mathematics 10, no. 12 (June 11, 2022): 2026. http://dx.doi.org/10.3390/math10122026.
Full textDissertations / Theses on the topic "Machine learning, kernel methods"
Tsang, Wai-Hung. "Kernel methods in supervised and unsupervised learning /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSANG.
Full textIncludes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.
Chen, Xiaoyi. "Transfer Learning with Kernel Methods." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0005.
Full textTransfer Learning aims to take advantage of source data to help the learning task of related but different target data. This thesis contributes to homogeneous transductive transfer learning where no labeled target data is available. In this thesis, we relax the constraint on conditional probability of labels required by covariate shift to be more and more general, based on which the alignment of marginal probabilities of source and target observations renders source and target similar. Thus, firstly, a maximum likelihood based approach is proposed. Secondly, SVM is adapted to transfer learning with an extra MMD-like constraint where Maximum Mean Discrepancy (MMD) measures this similarity. Thirdly, KPCA is used to align data in a RKHS on minimizing MMD. We further develop the KPCA based approach so that a linear transformation in the input space is enough for a good and robust alignment in the RKHS. Experimentally, our proposed approaches are very promising
Wu, Zhili. "Kernel based learning methods for pattern and feature analysis." HKBU Institutional Repository, 2004. http://repository.hkbu.edu.hk/etd_ra/619.
Full textBraun, Mikio Ludwig. "Spectral properties of the kernel matrix and their relation to kernel methods in machine learning." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=978607309.
Full textSamo, Yves-Laurent Kom. "Advances in kernel methods : towards general-purpose and scalable models." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:e0ff5f8c-bc28-4d96-8ddb-2d49152b2eee.
Full textLee, Dong Ryeol. "A distributed kernel summation framework for machine learning and scientific applications." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44727.
Full textVishwanathan, S. V. N. "Kernel Methods Fast Algorithms and real life applications." Thesis, Indian Institute of Science, 2003. http://hdl.handle.net/2005/49.
Full textChu, C. Y. C. "Pattern recognition and machine learning for magnetic resonance images with kernel methods." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18519/.
Full textRowland, Mark. "Structure in machine learning : graphical models and Monte Carlo methods." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/287479.
Full textQue, Qichao. "Integral Equations For Machine Learning Problems." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461258998.
Full textBooks on the topic "Machine learning, kernel methods"
Bernhard, Schölkopf, Burges Christopher J. C, and Smola Alexander J, eds. Advances in kernel methods: Support vector learning. Cambridge, Mass: MIT Press, 1999.
Find full textSuzuki, Joe. Kernel Methods for Machine Learning with Math and R. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0398-4.
Full textSuzuki, Joe. Kernel Methods for Machine Learning with Math and Python. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0401-1.
Full textCamps-Valls, Gustavo. Kernel methods for remote sensing 1: Data analysis 2. Hoboken, NJ: Wiley, 2009.
Find full textLéon-Charles, Tranchevent, Moor Bart, Moreau Yves, and SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
Find full textHsieh, William Wei. Machine learning methods in the environmental sciences: Neural networks and kernels. Cambridge, UK: Cambridge University Press, 2009.
Find full textMachine learning methods in the environmental sciences: Neural networks and kernels. Cambridge, UK: Cambridge University Press, 2009.
Find full textYu, Shi, Léon-Charles Tranchevent, Bart De Moor, and Yves Moreau. Kernel-based Data Fusion for Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19406-1.
Full textLearning kernel classifiers: Theory and algorithms. Cambridge, Mass: MIT Press, 2002.
Find full textG, Carbonell Jaime, ed. Machine learning: Paradigms and methods. Cambridge, Mass: MIT Press, 1990.
Find full textBook chapters on the topic "Machine learning, kernel methods"
Mannor, Shie, Xin Jin, Jiawei Han, Xin Jin, Jiawei Han, Xin Jin, Jiawei Han, and Xinhua Zhang. "Kernel Methods." In Encyclopedia of Machine Learning, 566–70. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_430.
Full textSmola, Alexander J., and Bernhard Schölkopf. "Bayesian Kernel Methods." In Advanced Lectures on Machine Learning, 65–117. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36434-x_3.
Full textZhang, Xinhua. "Kernel Methods." In Encyclopedia of Machine Learning and Data Mining, 1–5. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_144-1.
Full textZhang, Xinhua. "Kernel Methods." In Encyclopedia of Machine Learning and Data Mining, 690–95. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_144.
Full textMontesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Reproducing Kernel Hilbert Spaces Regression and Classification Methods." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 251–336. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_8.
Full textPronobis, Wiktor, and Klaus-Robert Müller. "Kernel Methods for Quantum Chemistry." In Machine Learning Meets Quantum Physics, 25–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7_3.
Full textCollins, Michael. "Tutorial: Machine Learning Methods in Natural Language Processing." In Learning Theory and Kernel Machines, 655. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45167-9_47.
Full textSuzuki, Joe. "Kernel Computations." In Kernel Methods for Machine Learning with Math and R, 89–122. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0398-4_4.
Full textSuzuki, Joe. "Kernel Computations." In Kernel Methods for Machine Learning with Math and Python, 91–128. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0401-1_4.
Full textSuzuki, Joe. "Reproducing Kernel Hilbert Space." In Kernel Methods for Machine Learning with Math and R, 59–87. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0398-4_3.
Full textConference papers on the topic "Machine learning, kernel methods"
Ramazanli, Ilqar. "Nearest Neighbor outperforms Kernel-Kernel Methods for Distribution Regression." In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2022. http://dx.doi.org/10.1109/cacml55074.2022.00009.
Full textMelacci, Stefano, and Marco Gori. "Kernel Methods for Minimum Entropy Encoding." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.83.
Full textXue, Hui, Yu Song, and Hai-Ming Xu. "Multiple Indefinite Kernel Learning for Feature Selection." 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.2017/448.
Full textTrindade, Luis A., Hui Wang, William Blackburn, and Niall Rooney. "Text classification using word sequence kernel methods." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016983.
Full textDeen, Anjna Jayant, and Manasi Gyanchandani. "Machine Learning Kernel Methods for Protein Function Prediction." In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987852.
Full textJiang, Qingnan, Mingxuan Wang, Jun Cao, Shanbo Cheng, Shujian Huang, and Lei Li. "Learning Kernel-Smoothed Machine Translation with Retrieved Examples." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.579.
Full textDa-Nian Zheng, Jia-Xin Wang, Yan-Nan Zhao, and Ze-Hong Yang. "Reduced sets and fast approximation for kernel methods." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527681.
Full textXu, Yong, Bin Sun, Chong-yang Zhang, Zhong Jin, Chuan-cai Liu, and Jing-yu Yang. "An Implementation Framework for Kernel Methods with High-Dimensional Patterns." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258439.
Full textZhao, Ziyi, Dan Shi, Hong Huo, and Tao Fang. "Feature Encoding Methods Evaluation based on Multiple kernel Learning." In ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3195106.3195152.
Full textNguyen, Khanh. "Nonparametric Online Machine Learning with Kernels." 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.2017/758.
Full textReports on the topic "Machine learning, kernel methods"
Xu, Yuesheng. Adaptive Kernel Based Machine Learning Methods. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada588768.
Full textVesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1493534.
Full textZhang, Tong. Multi-Stage Convex Relaxation Methods for Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada580533.
Full textJesneck, Jonathan, and Joseph Lo. Modular Machine Learning Methods for Computer-Aided Diagnosis of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada430017.
Full textHedyehzadeh, Mohammadreza, Shadi Yoosefian, Dezfuli Nezhad, and Naser Safdarian. Evaluation of Conventional Machine Learning Methods for Brain Tumour Type Classification. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, June 2020. http://dx.doi.org/10.7546/crabs.2020.06.14.
Full textSemen, Peter M. A Generalized Approach to Soil Strength Prediction With Machine Learning Methods. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada464726.
Full textChernozhukov, Victor, Kaspar Wüthrich, and Yinchu Zhu. Exact and robust conformal inference methods for predictive machine learning with dependent data. The IFS, March 2018. http://dx.doi.org/10.1920/wp.cem.2018.1618.
Full textHemphill, Geralyn M. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1329544.
Full textMishra, Umakant, and Sagar Gautam. Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1891184.
Full textMartinez, Carianne, John P. Korbin, Kevin Matthew Potter, Emily Donahue, Jeremy David Gamet, and Matthew David Smith. Investigating Machine Learning Based X-Ray Computed Tomography Reconstruction Methods to Enhance the Accuracy of CT Scans. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1571551.
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