Support-vector networks

Cited by (1,253)

Items 201–300 of 1,253

1. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
   John C. Duchi, Alekh Agarwal, Martin J. Wainwright · 2010
2. TMVA - Toolkit for Multivariate Data Analysis
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3. Sampling Matters in Deep Embedding Learning
   Chao-Yuan Wu, Raghaven Manmatha, Alexander Johannes Smola, Philipp Krähenbühl · 2017 · IEEE/CVF International Conference on Computer Vision (ICCV)
4. Joint Unsupervised Learning of Deep Representations and Image Clusters
   Yang, Jianwei, Parikh, Devi, Batra, Dhruv · 2016 · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
5. Applications of machine learning in cancer prediction and prognosis.
   Joseph A Cruz, David Scott Wishart · 2007 · PubMed Central
6. A Survey of Text Classification Algorithms
   Charų C. Aggarwal, ChengXiang Zhai · 2012 · Springer eBooks
7. Optimization method based extreme learning machine for classification
   Guang-Bin Huang, Xiaojian Ding, Hongming Zhou · 2010 · Neurocomputing
8. Molecular contrastive learning of representations via graph neural networks
   Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani · 2022 · Nature Machine Intelligence
9. A novel deep learning approach for classification of EEG motor imagery signals
   Tabar, Yousef Rezaei, Halici, Ugur · 2017 · Journal of Neural Engineering
10. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine
    William DeGroat, Habiba Abdelhalim, Kush Patel, Dinesh Mendhe, Saman Zeeshan, Zeeshan Ahmed · 2024 · Scientific Reports
11. Support vector machine approach for protein subcellular localization prediction
    Sujun Hua, Zhirong Sun · 2001 · Bioinformatics
12. Memory Fusion Network for Multi-view Sequential Learning
    Zadeh, Amir, Liang, Paul Pu, Mazumder, Navonil, Poria, Soujanya, Cambria, Erik, Morency, Louis-Philippe · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
13. From DFT to machine learning: recent approaches to materials science–a review
    Schleder, Gabriel R, Padilha, Antonio C M, Acosta, Carlos Mera, Costa, Marcio, Fazzio, Adalberto · 2019 · Journal of Physics: Materials
14. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
    Keith, John A., Vassilev-Galindo, Valentin, Cheng, Bingqing, Chmiela, Stefan, Gastegger, Michael, Müller, Klaus-Robert, Tkatchenko, Alexandre · 2021 · Chemical Reviews
15. Applications of Deep Learning and Reinforcement Learning to Biological Data
    Mufti Mahmud, M. Shamim Kaiser, Amir Hussain, Stefano Vassanelli · 2018 · IEEE Transactions on Neural Networks and Learning Systems
16. Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
    Frank, Mario, Biedert, Ralf, Ma, Eugene, Martinovic, Ivan, Song, Dawn · 2013 · IEEE Transactions on Information Forensics and Security
17. A User’s Guide to Support Vector Machines
    Asa Ben‐Hur, Jason Weston · 2009 · Methods in molecular biology
18. Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001
    Bing Chen, M.-W. Chang, Chih‐Jen Lin · 2004 · IEEE Transactions on Power Systems
19. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
    Hasan, Mahmudul, Islam, Md. Milon, Zarif, Md Ishrak Islam, Hashem, M.M.A. · 2019 · Internet of Things
20. State of the art in building modelling and energy performances prediction: A review
    Aurélie Foucquier, Sylvain Robert, Frédéric Suard, Louis Stéphan, Arnaud Jay · 2013 · Renewable and Sustainable Energy Reviews
21. Semi-Supervised Support Vector Machines
    Kristin P. Bennett, Ayhan Demiriz · 1998
22. Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb
    Mohammadreza Asghari Oskoei, Huosheng Hu · 2008 · IEEE Transactions on Biomedical Engineering
23. A survey of kernel and spectral methods for clustering
    Maurizio Filippone, Francesco Camastra, Francesco Masulli, Stefano Rovetta A · 2007 · Pattern Recognition
24. Dependency tree kernels for relation extraction
    Aron Culotta, Jeffrey S. Sorensen · 2004 · Proceedings of the conference - Association for Computational Linguistics. Meeting
25. Convolution Kernels for Natural Language
    Michael Collins, Nigel Duffy · 2002 · The MIT Press eBooks
26. Support vector regression
    Fan Zhang, Lauren J. O’Donnell · 2019 · Elsevier eBooks
27. Flood hazard risk assessment model based on random forest
    Zhaoli Wang, Chengguang Lai, Xiaohong Chen, Bing Kuan Yang, Shiwei Zhao, Xiaoyan Bai · 2015 · Journal of Hydrology
28. Leafsnap: A Computer Vision System for Automatic Plant Species Identification
    Neeraj Kumar, Peter N. Belhumeur, Arijit Biswas, David W. Jacobs, W. John Kress, Ida C. Lopez, João V. B. Soares · 2012 · Lecture notes in computer science
29. Predictive data mining in clinical medicine: Current issues and guidelines
    BELLAZZI, R, ZUPAN, B · 2008 · International Journal of Medical Informatics
30. Feature Selection for Classification of Hyperspectral Data by SVM
    Mahesh Pal, Giles M. Foody · 2010 · IEEE Transactions on Geoscience and Remote Sensing
31. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome
    Davoli, Teresa, Xu, Andrew Wei, Mengwasser, Kristen E., Sack, Laura M., Yoon, John C., Park, Peter J., Elledge, Stephen J. · 2013 · Cell
32. Support vector machines for 3D object recognition
    Massimiliano Pontil, Alessandro Verri · 1998 · IEEE Transactions on Pattern Analysis and Machine Intelligence
33. Age and Gender Estimation of Unfiltered Faces
    Eran Eidinger, Roee Enbar, Tal Hassner · 2014 · IEEE Transactions on Information Forensics and Security
34. An application of support vector machines in bankruptcy prediction model
    Kyung-Shik Shin, Taik Soo Lee, Hyun-Jung Kim · 2004 · Expert Systems with Applications
35. Genome-wide association study identifies multiple susceptibility loci for pulmonary fibrosis
    Fingerlin, Tasha E, Murphy, Elissa, Zhang, Weiming, Peljto, Anna L, Brown, Kevin K, Steele, Mark P, Loyd, James E, Cosgrove, Gregory P, Lynch, David, Groshong, Steve et al. · 2013 · Nature Genetics
36. Modeling and forecasting building energy consumption: A review of data-driven techniques
    Mathieu Bourdeau, Xiao qiang Zhai, Elyes Nefzaoui, Xiaofeng Guo, Patrice Chatellier · 2019 · Sustainable Cities and Society
37. Semi-Supervised and Unsupervised Extreme Learning Machines
    Gao Ming Huang, Shiji Song, Jatinder N.D. Gupta, Cheng Wu · 2014 · IEEE Transactions on Cybernetics
38. Review of Image Classification Algorithms Based on Convolutional Neural Networks
    Leiyu Chen, Shaobo Li, Qiang Bai, Jing Yang, Sanlong Jiang, Yanming Miao · 2021 · Remote Sensing
39. A support vector method for multivariate performance measures
    Thorsten Joachims · 2005 · International Conference on Machine Learning
40. Dominant Local Binary Patterns for Texture Classification
    Shu Liao, Max W. K. Law, Albert C. S. Chung · 2009 · IEEE Transactions on Image Processing
41. What Is Machine Learning?
    Issam El Naqa, Martin J. Murphy · 2015 · Springer eBooks
42. Fast Marginal Likelihood Maximisation for Sparse Bayesian Models
    Michael E. Tipping, Anita Faul · 2003
43. Support vector machine applications in the field of hydrology: A review
    Sujay Raghavendra Naganna, Paresh Chandra Deka · 2014 · Applied Soft Computing
44. Data Mining and Analysis: Fundamental Concepts and Algorithms
    Mohammed Javeed Zaki, Wagner Meira · 2014
45. A new learning paradigm: Learning using privileged information
    Vapnik, Vladimir, Vashist, Akshay · 2009 · Neural Networks
46. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
    Yufei Xia, Chuanzhe Liu, Yuying Li, Nana Liu · 2017 · Expert Systems with Applications
47. A survey of intrusion detection techniques for cyber-physical systems
    Mitchell, Robert, Chen, Ing-Ray · 2014 · ACM Computing Surveys
48. Why neurons mix: high dimensionality for higher cognition
    Fusi, Stefano, Miller, Earl K, Rigotti, Mattia · 2016 · Current Opinion in Neurobiology
49. Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue
    Qiang Ji, Zhiwei Zhu, Ping Lan · 2004 · IEEE Transactions on Vehicular Technology
50. Structural imprints in vivo decode RNA regulatory mechanisms
    Robert C. Spitale, Ryan A. Flynn, Qiangfeng Cliff Zhang, Pete Crisalli, Byron Lee, Jong‐Wha Jung, Hannes Y. Kuchelmeister, Pedro J. Batista, Eduardo A. Torre, Eric T. Kool et al. · 2015 · Nature
51. Machine Learning for Structural Engineering: A State-of-the-Art Review
    Thai, Huu-Tai · 2022 · Structures
52. A Structure-Based Drug Discovery Paradigm
    Maria Batool, Sheikh Bilal Ahmad, Sangdun Choi · 2019 · International Journal of Molecular Sciences
53. Estimating Individualized Treatment Rules Using Outcome Weighted Learning
    Ying‐Qi Zhao, Donglin Zeng, Augustus John Rush, Michael R. Kosorok · 2012 · Journal of the American Statistical Association
54. Controlling an organic synthesis robot with machine learning to search for new reactivity
    Jarosław M. Granda, Liva Donina, Vincenza Dragone, De‐Liang Long, Leroy Cronin · 2018 · Nature
55. Tissue Classification with Gene Expression Profiles
    Amir Ben‐Dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Michèl Schummer, Zohar Yakhini B · 2000 · Journal of Computational Biology
56. A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
    Preeti Mishra, Vijay Varadharajan, Udaya Kiran Tupakula, Emmanuel S. Pilli · 2018 · IEEE Communications Surveys & Tutorials
57. Perspectives in machine learning for wildlife conservation
    Tuia, Devis, Kellenberger, Benjamin, Beery, Sara, Costelloe, Blair R., Zuffi, Silvia, Risse, Benjamin, Mathis, Alexander, Mathis, Mackenzie W., van Langevelde, Frank, Burghardt, Tilo et al. · 2022 · Nature Communications
58. Chemical gas sensor drift compensation using classifier ensembles
    Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret Amy Ryan, M. L. Homer, Ramón Huerta · 2012 · Sensors and Actuators B Chemical
59. Directed Evolution: Methodologies and Applications
    Yajie Wang, Pu Xue, Mingfeng Cao, Tianhao Yu, Stephan Lane, Huimin Zhao · 2021 · Chemical Reviews
60. SurroundSense
    Martin Azizyan, Ionut Constandache, Romit Roy Choudhury · 2009 · ACM Annual International Conference on Mobile Computing and Networking (MobiCom)
61. Detecting depression and mental illness on social media: an integrative review
    Guntuku, Sharath Chandra, Yaden, David B, Kern, Margaret L, Ungar, Lyle H, Eichstaedt, Johannes C · 2017 · Current Opinion in Behavioral Sciences
62. A Comprehensive Survey of Loss Functions in Machine Learning
    Qi Wang, Yue Ma, Kun Zhao, Yingjie Tian · 2020 · Annals of Data Science
63. Understanding Random Forests: From Theory to Practice
    Gilles Louppe · 2014 · arXiv (Cornell University)
64. Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions
    Cioffi, Raffaele, Travaglioni, Marta, Piscitelli, Giuseppina, Petrillo, Antonella, De Felice, Fabio · 2020 · Sustainability
65. Decoding Neural Representational Spaces Using Multivariate Pattern Analysis
    Haxby, James V., Connolly, Andrew C., Guntupalli, J. Swaroop · 2014 · Annual Review of Neuroscience
66. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers
    Martin Weinmann A, B. Jutzi, Stefan Hinz, Clément Mallet · 2015 · ISPRS Journal of Photogrammetry and Remote Sensing
67. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression
    Taşkın Kavzoğlu, Emrehan Kutluğ Şahin, İsmail Çölkesen · 2013 · Landslides
68. Boosting the margin: A new explanation for the effectiveness of voting methods
    Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee · 1997
69. Support vector machines
    Alessia Mammone, Marco Turchi, Nello Cristianini · 2009 · Wiley Interdisciplinary Reviews Computational Statistics
70. Challenges and opportunities in quantum machine learning
    Marco Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Łukasz Cincio, Patrick J. Coles · 2022 · Nature Computational Science
71. Self-Driving Laboratories for Chemistry and Materials Science
    Tom, Gary, Schmid, Stefan P., Baird, Sterling G., Cao, Yang, Darvish, Kourosh, Hao, Han, Lo, Stanley, Pablo-García, Sergio, Rajaonson, Ella M., Skreta, Marta et al. · 2024 · Chemical Reviews
72. Support Vector Machines and Kernels for Computational Biology
    Asa Ben‐Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, Gunnar Rätsch · 2008 · PLoS Computational Biology
73. Machine Learning: The art and science of algorithms that make sense of data
    Peter A. Flach · 2012
74. Challenges in representation learning: A report on three machine learning contests
    Goodfellow, Ian J., Erhan, Dumitru, Luc Carrier, Pierre, Courville, Aaron, Mirza, Mehdi, Hamner, Ben, Cukierski, Will, Tang, Yichuan, Thaler, David, Lee, Dong-Hyun et al. · 2015 · Neural Networks
75. Use of the zero norm with linear models and kernel methods
    Jason Weston, André Elisseeff, Bernhard Schölkopf, Mike Tipping · 2003 · MPG.PuRe (Max Planck Society)
76. Artificial intelligence and machine learning in design of mechanical materials
    Kai Guo, Zhenze Yang, Chi‐Hua Yu, Markus J. Buehler · 2020 · Materials Horizons
77. A kernel functions analysis for support vector machines for land cover classification
    Taşkın Kavzoğlu, İsmail Çölkesen · 2009 · International Journal of Applied Earth Observation and Geoinformation
78. The Support Vector Method of Function Estimation
    Vladimir N. Vapnik · 1998 · Springer eBooks
79. A Comprehensive Review on Current Advances in Peptide Drug Development and Design
    Andy Chi-Lung Lee, Janelle Louise Harris, Kum Kum Khanna, Ji‐Hong Hong · 2019 · International Journal of Molecular Sciences
80. Support vector machines
    Kristin P. Bennett, Colin Campbell · 2000 · ACM SIGKDD Explorations Newsletter
81. NLOS identification and mitigation for localization based on UWB experimental data
    Stefano Maranò, Wesley M. Gifford, Henk Wymeersch, Moe Z. Win · 2010 · IEEE Journal on Selected Areas in Communications
82. A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
    Dazhong Wu, Connor Jennings, Janis P. Terpenny, Robert X. Gao, Soundar R. T. Kumara · 2017 · Journal of Manufacturing Science and Engineering
83. Towards Large-Scale Small Object Detection: Survey and Benchmarks
    Cheng, Gong, Yuan, Xiang, Yao, Xiwen, Yan, Kebing, Zeng, Qinghua, Xie, Xingxing, Han, Junwei · 2023 · IEEE Transactions on Pattern Analysis and Machine Intelligence
84. Efficient grasping from RGBD images: Learning using a new rectangle representation
    Jiang Yun, Stephen Moseson, Ashutosh Saxena · 2011 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
85. Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019
    Hou, Rongrong, Xia, Yong · 2021 · Journal of Sound and Vibration
86. PathogenFinder - Distinguishing Friend from Foe Using Bacterial Whole Genome Sequence Data
    Salvatore Cosentino, Mette Voldby Larsen, Frank M. Aarestrup, Ole Lund · 2013 · PLoS ONE
87. Problem formulations and solvers in linear SVM: a review
    Vinod Kumar Chauhan, Kalpana Dahiya, Anuj Sharma · 2018 · Artificial Intelligence Review
88. Deep learning in video multi-object tracking: A survey
    Ciaparrone, Gioele, Luque Sánchez, Francisco, Tabik, Siham, Troiano, Luigi, Tagliaferri, Roberto, Herrera, Francisco · 2020 · Neurocomputing
89. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
    Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C. Bulusu, Günter Klambauer · 2017 · Bioinformatics
90. Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights
    Pasolli, Edoardo, Truong, Duy Tin, Malik, Faizan, Waldron, Levi, Segata, Nicola · 2016 · PLOS Computational Biology
91. A review of unsupervised feature selection methods
    Saúl Solorio-Fernández, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad · 2019 · Artificial Intelligence Review
92. A survey on river water quality modelling using artificial intelligence models: 2000–2020
    Tiyasha Tiyasha, Tran Minh Tung, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬ · 2020 · Journal of Hydrology
93. On the Learnability and Design of Output Codes for Multiclass Problems
    Koby Crammer, Yoram Singer · 2002 · Machine Learning
94. A Decade Survey of Transfer Learning (2010–2020)
    Shuteng Niu, Yongxin Liu, Jian Wang, Houbing Song · 2020 · IEEE Transactions on Artificial Intelligence
95. Human fall detection on embedded platform using depth maps and wireless accelerometer
    Kwolek, Bogdan, Kepski, Michal · 2014 · Computer Methods and Programs in Biomedicine
96. PredGPI: a GPI-anchor predictor
    Andrea Pierleoni, Pier Luigi Martelli, Rita Casadio · 2008 · BMC Bioinformatics
97. Evaluation of simple performance measures for tuning SVM hyperparameters
    Kai-Bo Duan, S. Sathiya Keerthi, AUN NEOW POO · 2003 · Neurocomputing
98. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
    Abdulhakim M. Abdi · 2019 · GIScience & Remote Sensing
99. Rewrite the Stars
    Xu Ma, Xiyang Dai, Yue Bai, Yizhou Wang, Yun Fu · 2024 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
100. SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
     José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera · 2014 · Information Sciences