Support-vector networks

Cited by (1,254)

Items 101–200 of 1,254

1. Kernel-based methods for hyperspectral image classification
   Gustau Camps‐Valls, Lorenzo Bruzzone · 2005 · IEEE Transactions on Geoscience and Remote Sensing
2. Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
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3. Working Set Selection Using Second Order Information for Training Support Vector Machines
   Rong-En Fan, Pai-Hsuen Chen, Chih‐Jen Lin · 2005 · Journal of Machine Learning Research
4. KERNEL METHODS IN MACHINE LEARNING
   Thomas Hofmann, Bernhard Schölkopf, Alexander Johannes Smola · 2008
5. Comparing support vector machines with Gaussian kernels to radial basis function classifiers
   Bernhard Schölkopf, Kah-Kay Sung, Chris J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, Vladimir N. Vapnik · 1997 · IEEE Transactions on Signal Processing
6. Attribute and simile classifiers for face verification
   Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, Shree K. Nayar · 2009 · IEEE/CVF International Conference on Computer Vision (ICCV)
7. Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
   Min, Bonan, Ross, Hayley, Sulem, Elior, Veyseh, Amir Pouran Ben, Nguyen, Thien Huu, Sainz, Oscar, Agirre, Eneko, Heintz, Ilana, Roth, Dan · 2024 · ACM Computing Surveys
8. Review of photovoltaic power forecasting
   J. Antonanzas, Natalia Osorio, Rodrigo A. Escobar, Ruben Urraca-Valle, Francisco Javier Martínez-de-Pisón, Fernando Antoñanzas-Torres · 2016 · Solar Energy
9. Multivariate Analysis in Metabolomics
   Bradley Worley, Robert Powers · 2012 · Current Metabolomics
10. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
    Chaopeng Shen · 2018 · Water Resources Research
11. Machine learning in manufacturing: Advantages, challenges, and applications
    Wuest, Thorsten, Weimer, Daniel, Irgens, Christopher, Thoben, Klaus-Dieter · 2016 · Production & Manufacturing Research
12. DianNao
    Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, Olivier Temam · 2014 · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)
13. RANDOM SURVIVAL FORESTS
    Hemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone, Michael S. Lauer · 2008
14. A brief introduction to boosting
    Robert E. Schapire · 1999
15. Soft Margins for AdaBoost
    Gunnar Rätsch, Takashi Onoda, K. Robert Müller · 2001 · Machine Learning
16. Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification
    Weihua Chen, Xiaotang Chen, Jianguo Zhang, Kaiqi Huang · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
17. Machine learning in medicine: a practical introduction
    Jenni A. M. Sidey-Gibbons, Chris J. Sidey-Gibbons · 2019 · BMC Medical Research Methodology
18. Materials discovery and design using machine learning
    Liu, Yue, Zhao, Tianlu, Ju, Wangwei, Shi, Siqi · 2017 · Journal of Materiomics
19. Machine learning for data-driven discovery in solid Earth geoscience
    Karianne J. Bergen, Paul Allan Johnson, Maarten V. de Hoop, Gregory C. Beroza · 2019 · Science
20. OSQP: an operator splitting solver for quadratic programs
    Bartolomeo Stellato, Goran Banjac, Paul J. Goulart, Alberto Bemporad, Stephen Boyd · 2020 · Mathematical Programming Computation
21. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
    Abdelaziz Merghadi, Ali P. Yunus, Jie Dou, Jim Whiteley, Binh Thai Pham, Dieu Tien Bui, Ram Avtar, Abderrahmane Boumezbeur · 2020 · Earth-Science Reviews
22. EEG signal classification using PCA, ICA, LDA and support vector machines
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23. Large Scale Multiple Kernel Learning
    Sören Sonnenburg, Gunnar Rätsch, Christin Schäfer, Bernhard Schölkopf · 2006 · MPG.PuRe (Max Planck Society)
24. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
    Biswajeet Pradhan · 2012 · Computers & Geosciences
25. Regularization Networks and Support Vector Machines
    Theodoros Evgeniou, Massimiliano Pontil, Tomaso Poggio · 2000 · Advances in Computational Mathematics
26. End-to-End Incremental Learning
    Castro, Francisco M., Marín-Jiménez, Manuel J., Guil, Nicolás, Schmid, Cordelia, Alahari, Karteek · 2018 · Lecture Notes in Computer Science
27. Age and gender classification using convolutional neural networks
    Gil Levi, Tal Hassncer · 2015 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
28. Efficient Low-rank Multimodal Fusion With Modality-Specific Factors
    Liu, Zhun, Shen, Ying, Lakshminarasimhan, Varun Bharadhwaj, Liang, Paul Pu, Bagher Zadeh, AmirAli, Morency, Louis-Philippe · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
29. Machine Learning Methods That Economists Should Know About
    Athey, Susan, Imbens, Guido W. · 2019 · Annual Review of Economics
30. Data-Driven Science and Engineering
    Steven L. Brunton, J. Nathan Kutz · 2019 · Cambridge University Press eBooks
31. Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
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32. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies
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33. SMOTE for high-dimensional class-imbalanced data
    Rok Blagus, Lara Lusa · 2013 · BMC Bioinformatics
34. Protein function prediction via graph kernels
    Karsten Michael Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander Johannes Smola, H.-P. Kriegel · 2005 · Bioinformatics
35. Pattern Classification
    Ben Gold, Nelson H. Morgan, Daniel P. W. Ellis · 2011
36. Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
    Mohammadreza Sheykhmousa, Masoud Mahdianpari, Hamid Ghanbari, Fariba Mohammadimanesh, Pedram Ghamisi, Saeid Homayouni · 2020 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
37. Applications of Machine Learning in Cancer Prediction and Prognosis
    Joseph A. Cruz, David Scott Wishart · 2006 · Cancer Informatics
38. Support vector machines for classification in remote sensing
    Mahesh Pal, Paul M. Mather · 2005 · International Journal of Remote Sensing
39. Machine-learning-guided directed evolution for protein engineering
    Kevin Yang, Zachary Wu, Frances H. Arnold · 2019 · Nature Methods
40. Large margin classification using the perceptron algorithm
    Yoav Freund, Robert E. Schapire · 1998 · Conference on Learning Theory
41. Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
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42. Convexity, Classification, and Risk Bounds
    Peter L. Bartlett, Michael I. Jordan, Jon McAuliffe · 2006 · Journal of the American Statistical Association
43. The accuracy, fairness, and limits of predicting recidivism
    Dressel, Julia, Farid, Hany · 2018 · Science Advances
44. Machine learning in materials science
    Wei, Jing, Chu, Xuan, Sun, Xiang‐Yu, Xu, Kun, Deng, Hui‐Xiong, Chen, Jigen, Wei, Zhongming, Lei, Ming · 2019 · InfoMat
45. Accurate predictions on small data with a tabular foundation model
    Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter · 2025 · Nature
46. An improved training algorithm for support vector machines
    E. Osuna, Robert M. Freund, Federico Girosi · 2002 · Machine learning for signal processing ...
47. Vision-Language Models for Vision Tasks: A Survey
    Zhang, Jingyi, Huang, Jiaxing, Jin, Sheng, Lu, Shijian · 2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence
48. Recent Advances in Open Set Recognition: A Survey
    Chuanxing Geng, Sheng-Jun Huang, Songcan Chen · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
49. Generalizing to unseen domains: A survey on domain generalization
    Wang, Jindong, Lan, Cuiling, Liu, Chang, Ouyang, Yidong, Qin, Tao, Lu, Wang, Chen, Yiqiang, Zeng, Wenjun, Yu, Philip · 2022 · IEEE Transactions on Knowledge and Data Engineering
50. Support Vector Machines for classification and regression
    Richard G. Brereton, Gavin Rhys Lloyd · 2009 · The Analyst
51. Support vector tracking
    Shai Avidan · 2004 · IEEE Transactions on Pattern Analysis and Machine Intelligence
52. Deep learning based multi-temporal crop classification
    Liheng Zhong, Lina Hu, Hang Zhou · 2018 · Remote Sensing of Environment
53. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
    Xin Yu Yang, Yifei Wang, Ryan Byrne, Gisbert Schneider, Shengyong Yang · 2019 · Chemical Reviews
54. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
    Mahyat Shafapour Tehrany, Biswajeet Pradhan, Mustafa Neamah Jebur · 2014 · Journal of Hydrology
55. Credit rating analysis with support vector machines and neural networks: a market comparative study
    Zan Huang, Hsinchun Chen, Chia‐Jung Hsu, Wun-Hwa Chen, Soushan Wu · 2003 · Decision Support Systems
56. Combining SVMs with Various Feature Selection Strategies
    Yiwei Chen, Chih‐Jen Lin · 2008 · Studies in fuzziness and soft computing
57. Machine Learning for Internet of Things Data Analysis: A Survey
    Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Mamaani Barnaghi, Amit Sheth · 2017 · Surrey Research Insight Open Access (The University of Surrey)
58. The Transporter Classification Database (TCDB): recent advances
    Milton H. Saier, Vamsee S. Reddy, Brian V. Tsu, Muhammad Saad Ahmed, Chun Li, Gabriel Moreno‐Hagelsieb · 2015 · Nucleic Acids Research
59. Federated learning of predictive models from federated Electronic Health Records
    Brisimi, Theodora S., Chen, Ruidi, Mela, Theofanie, Olshevsky, Alex, Paschalidis, Ioannis Ch., Shi, Wei · 2018 · International Journal of Medical Informatics
60. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery
    Dennis C. Duro, Steven E. Franklin, Monique G. Dubé · 2011 · Remote Sensing of Environment
61. Probability Estimates for Multi-class Classification by Pairwise Coupling
    Tingfan Wu, Chih‐Jen Lin, Ruby C. Weng, Yoram Singer · 2003
62. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
    Luo, Wei, Phung, Dinh, Tran, Truyen, Gupta, Sunil, Rana, Santu, Karmakar, Chandan, Shilton, Alistair, Yearwood, John, Dimitrova, Nevenka, Ho, Tu Bao et al. · 2016 · Journal of Medical Internet Research
63. A survey of unsupervised deep domain adaptation
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64. Soybean yield prediction from UAV using multimodal data fusion and deep learning
    Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike, Sean Hartling, Flavio Esposito, Felix Fritschi · 2019 · Remote Sensing of Environment
65. Auto-encoder based dimensionality reduction
    Yasi Wang, Hongxun Yao, Sicheng Zhao · 2015 · Neurocomputing
66. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance
    Till Rumpf, Anne‐Katrin Mahlein, Ulrike Steiner, Erich-Christian Oerke, H. W. Dehne, Lutz Plümer · 2010 · Computers and Electronics in Agriculture
67. Pedestrian Detection via Classification on Riemannian Manifolds
    Oncel Tuzel, Fatih Porikli, Peter Meer · 2008 · IEEE Transactions on Pattern Analysis and Machine Intelligence
68. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
    Yu, Kun-Hsing, Zhang, Ce, Berry, Gerald J., Altman, Russ B., Ré, Christopher, Rubin, Daniel L., Snyder, Michael · 2016 · Nature Communications
69. Cutting-plane training of structural SVMs
    T. Joachims, Thomas Finley, C. Yu · 2009 · Machine-mediated learning
70. Machine Learning Approaches for Clinical Psychology and Psychiatry
    Dwyer, Dominic B., Falkai, Peter, Koutsouleris, Nikolaos · 2018 · Annual Review of Clinical Psychology
71. An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
    Guang-Bin Huang · 2014 · Cognitive Computation
72. <scp>eD</scp> octor: machine learning and the future of medicine
    Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., Asadi, H. · 2018 · Journal of Internal Medicine
73. Translational biomarker discovery in clinical metabolomics: an introductory tutorial
    Jianguo Xia, David Broadhurst, Michael Wilson, David Scott Wishart · 2012 · Metabolomics
74. Machine learning models and bankruptcy prediction
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75. A Model Predictive Control Approach to Microgrid Operation Optimization
    Alessandra Parisio, Evangelos Rikos, Luigi Glielmo · 2014 · IEEE Transactions on Control Systems Technology
76. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
    Duchi, J. C., Agarwal, A., Wainwright, M. J. · 2012 · IEEE Transactions on Automatic Control
77. Classification using intersection kernel support vector machines is efficient
    Subhransu Maji, Alexander C. Berg, Jitendra Malik · 2008 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
78. Improving support vector machine classifiers by modifying kernel functions
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79. Untitled work
    2007
80. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion
    Fu‐Chen Chen, Mohammad R. Jahanshahi · 2017 · IEEE Transactions on Industrial Electronics
81. A comprehensive survey on sentiment analysis: Approaches, challenges and trends
    Marouane Birjali, Mohammed Kasri, Abderrahim Beni‐Hssane · 2021 · Knowledge-Based Systems
82. K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
    José Jiménez-Luna, Miha Škalič, Gerard Martínez-Rosell, Gianni De Fabritiis · 2018 · Journal of Chemical Information and Modeling
83. Data Mining and Analytics in the Process Industry: The Role of Machine Learning
    Ge, Zhiqiang, Song, Zhihuan, Ding, Steven X., Huang, Biao · 2017 · IEEE Access
84. Machine learning for internet of things data analysis: a survey
    Mahdavinejad, Mohammad Saeid, Rezvan, Mohammadreza, Barekatain, Mohammadamin, Adibi, Peyman, Barnaghi, Payam, Sheth, Amit P. · 2018 · Digital Communications and Networks
85. MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
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86. A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding
    Piotr S. Gromski, Howbeer Muhamadali, David I. Ellis, Yun Xu A, Elon Santos Correa, Michael Lewis Turner, Royston Goodacre · 2015 · Analytica Chimica Acta
87. Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis
    Sayanan Sivaraman, Mohan Manubhai Trivedi · 2013 · IEEE Transactions on Intelligent Transportation Systems
88. Optimization for Machine Learning
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89. Local Binary Patterns and Its Application to Facial Image Analysis: A Survey
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90. Designing Theory-Driven User-Centric Explainable AI
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91. Introduction to Online Convex Optimization
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92. Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
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93. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
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94. Deep Bayesian Active Learning with Image Data
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95. Power of data in quantum machine learning
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96. Scaling Vision Transformers
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97. Feature Selection: A Data Perspective
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98. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
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99. A data-driven approach to predict the success of bank telemarketing
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100. Generalized Out-of-Distribution Detection: A Survey
     Yang, Jingkang, Zhou, Kaiyang, Li, Yixuan, Liu, Ziwei · 2024 · International Journal of Computer Vision