Support-vector networks

Cited by (1,252)

Items 701–800 of 1,252

1. Landslide susceptibility evaluation and hazard zonation techniques – a review
   Leulalem Shano, Tarun Kumar Raghuvanshi, Matebie Meten · 2020 · Geoenvironmental Disasters
2. Attention mechanism-based CNN for facial expression recognition
   Jing Li, Kan Jin, Dalin Zhou, Naoyuki Kubota, Zhaojie Ju · 2020 · Neurocomputing
3. Transformation Invariance in Pattern Recognition – Tangent Distance and Tangent Propagation
   Patrice Y. Simard, Yann LeCun, John S. Denker, Bernard Victorri · 2012 · Lecture notes in computer science
4. TILDE: A Temporally Invariant Learned DEtector
   Yannick Verdié, Kwang Moo Yi, Pascal Fua, Vincent Lepetit · 2015 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
5. Atlas of clinically distinct cell states and ecosystems across human solid tumors
   Bogdan A. Luca, Chloé Beate Steen, Magdalena Matusiak, Armon Azizi, Sushama Varma, Chunfang Zhu, Joanna Przybył, Almudena Espín-Pérez, Maximilian Diehn, Ash A. Alizadeh et al. · 2021 · Cell
6. Support vector regression
   Dirmanto Jap, Marc Stöttinger, Shivam Bhasin · 2015 · Hardware and Architectural Support for Security and Privacy
7. Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review
   Mohammad Azhar Mat Daut, Mohammad Yusri Hassan, Hayati Abdullah, Hasimah Abdul Rahman, Md Pauzi Abdullah, Faridah Hussin · 2016 · Renewable and Sustainable Energy Reviews
8. Automatic Speech Recognition: A Survey
   Malik, Mishaim, Malik, Muhammad Kamran, Mehmood, Khawar, Makhdoom, Imran · 2021 · Multimedia Tools and Applications
9. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
   Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus‐Robert Müller · 2021 · Nature Communications
10. Feature selection for support vector machines by means of genetic algorithm
    Holger Fröhlich, Olivier Chapelle, Bernhard Schölkopf · 2004 · IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
11. Support Vector Machine Solvers
    Léon Bottou, Chih‐Jen Lin · 2007 · The MIT Press eBooks
12. Sequential Fault Diagnosis Based on LSTM Neural Network
    Haitao Zhao, Shaoyuan Sun, Bo Jin · 2018 · IEEE Access
13. A review on sentiment analysis from social media platforms
    Margarita Rodríguez-Ibáñez, Antonio Casánez-Ventura, Félix Castejón-Mateos, Pedro-Manuel Cuenca-Jiménez · 2023 · Expert Systems with Applications
14. Multicategory Classification by Support Vector Machines
    Erin J. Bredensteiner, Kristin P. Bennett · 1999 · Computational Optimization and Applications
15. Big Data sources and methods for social and economic analyses
    Amparo Blazquez-Soriano, Josep Domènech · 2017 · Technological Forecasting and Social Change
16. Evaluation of machine learning methods for formation lithology identification: A comparison of tuning processes and model performances
    Yunxin Xie, Chenyang Zhu, Wen Xin Zhou, Zhongdong Li, Xuan Liu, Mei Tu · 2017 · Journal of Petroleum Science and Engineering
17. Application of machine learning in groundwater quality modeling - A comprehensive review
    Ryan Haggerty, Jianxin Sun, Hongfeng Yu, Yusong Li · 2023 · Water Research
18. Flood susceptibility mapping using convolutional neural network frameworks
    Yi Wang, Zhice Fang, Haoyuan Hong, Ling Peng · 2019 · Journal of Hydrology
19. Unveiling the predictive power of static structure in glassy systems
    Victor Bapst, Thomas M. Keck, Agnieszka Grabska‐Barwińska, C. Donner, Ekin D. Cubuk, Samuel S. Schoenholz, Annette Obika, A. W. R. Nelson, Trevor Back, Demis Hassabis et al. · 2020 · Nature Physics
20. Effective Pattern Discovery for Text Mining
    Ning Zhong, Yuefeng Li, Sheng‐Tang Wu · 2010 · IEEE Transactions on Knowledge and Data Engineering
21. A Study on SMO-Type Decomposition Methods for Support Vector Machines
    Pai-Hsuen Chen, Rong-En Fan, Chih‐Jen Lin · 2006 · IEEE Transactions on Neural Networks
22. Machine learning directed drug formulation development
    Pauric Bannigan, Matteo Aldeghi, Zeqing Bao, Florian Häse, Alán Aspuru‐Guzik, Christine J. Allen · 2021 · Advanced Drug Delivery Reviews
23. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat
    Shuaipeng Fei, Muhammad Adeel Hassan, Yonggui Xiao, Xin Su, Zhen Chen, Qian Sheng Cheng, Fuyi Duan, Riqiang Chen, Yuntao Ma · 2022 · Precision Agriculture
24. Automated annotation of coral reef survey images
    Oscar Beijbom, Peter J. Edmunds, David I. Kline, B. Greg Mitchell, David Juris Kriegman · 2012 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
25. A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales
    Aniruddha Ghosh, Fabian Ewald Fassnacht, Pawan Kumar Joshi, Barbara Koch · 2013 · International Journal of Applied Earth Observation and Geoinformation
26. BaCelLo: a balanced subcellular localization predictor
    Andrea Pierleoni, Pier Luigi Martelli, Piero Fariselli, Rita Casadio · 2006 · Bioinformatics
27. PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine
    Ian Donaldson, Joel Martin, Berry de Bruijn, Cheryl D. Wolting, Vicki Lay, Brigitte A. Tuekam, Shudong Zhang, Berivan Baskin, Gary D. Bader, Katerina Michalickova et al. · 2003 · BMC Bioinformatics
28. A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders
    Ashish Mishra, M Shiva Krishna Reddy, Anurag Mittal, Hema A. Murthy · 2018 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
29. Support vector machines for face recognition
    Guodong Guo, Stan Ziqing Li, Kap Luk Chan · 2001 · Image and Vision Computing
30. Recurrent least squares support vector machines
    Johan A. K. Suykens, Joos P. L. Vandewalle · 2000 · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications
31. A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
    Ye Ren, Ponnuthurai Nagaratnam Suganthan, Narasimalu Srikanth · 2014 · IEEE Transactions on Sustainable Energy
32. Support vector machines and its applications in chemistry
    Hong‐Dong Li, Yi‐Zeng Liang, Qingsong Xu · 2008 · Chemometrics and Intelligent Laboratory Systems
33. Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution
    Özgür Kişi, Kulwinder Singh Parmar · 2015 · Journal of Hydrology
34. Fastfood: approximating kernel expansions in loglinear time
    Quoc Viet Le, Tamás Sarlós, Alex J. Smola · 2013
35. Deep learning for side-channel analysis and introduction to ASCAD database
    Ryad Benadjila, Emmanuel Prouff, Rémi Strullu, Eleonora Cagli, Cécile Dumas · 2019 · Journal of Cryptographic Engineering
36. DianNao
    Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, Olivier Temam · 2014 · ACM SIGARCH Computer Architecture News
37. Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
    Budak, Ceren, Goel, Sharad, Rao, Justin M. · 2016 · Public Opinion Quarterly
38. Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
    Levi, Gil, Hassner, Tal · 2015 · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction
39. A deep learning approach for the analysis of masses in mammograms with minimal user intervention
    Neeraj Dhungel, Gustavo Carneiro, Andrew Peter Bradley · 2017 · Medical Image Analysis
40. High dimensional data classification and feature selection using support vector machines
    Bissan Ghaddar, Joe Naoum‐Sawaya · 2017 · European Journal of Operational Research
41. Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods
    Han Wei, Shuaishuai Zhao, Qingyuan Rong, Hua Bao · 2018 · International Journal of Heat and Mass Transfer
42. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
    Chaopeng Shen, Eric Laloy, AMIN A. ELSHORBAGY, Adrian Albert, Jerad D. Bales, Fi‐John Chang, Sangram Ganguly, Kuolin Hsu, Daniel Kifer, Zheng Nick Fang et al. · 2018 · Hydrology and earth system sciences
43. Distinguishing computer graphics from natural images using convolution neural networks
    Nicolas Rahmouni, Vincent Nozick, Junichi Yamagishi, Isao Echizen · 2017 · IEEE International Workshop on Information Forensics and Security
44. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
    Gavin C. Cawley, Nicola L. C. Talbot · 2004 · Neural Networks
45. Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance
    Tuan Anh Ngo, Zhi Lu, Gustavo Carneiro · 2016 · Medical Image Analysis
46. Local Learning With Deep and Handcrafted Features for Facial Expression Recognition
    Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Marius Popescu · 2019 · IEEE Access
47. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease
    M. Tanveer, Bharat Richhariya, Rooh Ullah Khan, Ashraf Haroon Rashid, Pritee Khanna, Mukesh Prasad, Chin‐Teng Lin · 2020 · ACM Transactions on Multimedia Computing Communications and Applications
48. Mining customer requirements from online reviews: A product improvement perspective
    Jiayin Qi, Zhenping Zhang, Seongmin Jeon, Yanquan Zhou · 2016 · Information & Management
49. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging
    Yuhui Du, Zening Fu, Vince D. Calhoun · 2018 · Frontiers in Neuroscience
50. State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression
    Qianglong Li, Dezhi Li, Kun Zhao, Licheng Wang, Kai Wang · 2022 · Journal of Energy Storage
51. Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale
    Mustafa Neamah Jebur, Biswajeet Pradhan, Mahyat Shafapour Tehrany · 2014 · Remote Sensing of Environment
52. Feature selection methods for big data bioinformatics: A survey from the search perspective
    Lipo Wang, Yaoli Wang, Qing Chang · 2016 · Methods
53. Big data analytics for preventive medicine
    Imran Razzak, Muhammad Ali Imran, Guandong Xu · 2019 · Neural Computing and Applications
54. Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making
    Mohammad Pourhomayoun, Mahdi Shakibi · 2021 · Smart Health
55. Component-Based Face Recognition with 3D Morphable Models
    Benjamin Weyrauch, Bernd Heisele, J. Huang, Volker Blanz · 2005 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
56. The Effects of Data Quality on Machine Learning Performance on Tabular Data
    Mohammed, Sedir, Budach, Lukas, Feuerpfeil, Moritz, Ihde, Nina, Nathansen, Andrea, Noack, Nele, Patzlaff, Hendrik, Naumann, Felix, Harmouch, Hazar · 2025 · Information Systems
57. Monthly streamflow prediction using modified EMD-based support vector machine
    Shengzhi Huang, Jianxia Chang, Qiang Huang, Yutong Chen · 2014 · Journal of Hydrology
58. Silicon Photonic Modulator Neuron
    Alexander N. Tait, Thomas Ferreira de Lima, Mitchell A. Nahmias, Heidi B. Miller, Hsuan-Tung Peng, Bhavin J. Shastri, Paul R. Prucnal · 2019 · Physical Review Applied
59. Real-Time Detection of Traffic From Twitter Stream Analysis
    Eleonora D’Andrea, Pietro Ducange, Beatrice Lazzerini, Francesco Marcelloni · 2015 · IEEE Transactions on Intelligent Transportation Systems
60. Machine learning, statistical learning and the future of biological research in psychiatry
    Raquel Iniesta, Daniel R. Stahl, Peter McGuffin · 2016 · Psychological Medicine
61. Deep Learning Advances in Computer Vision with 3D Data
    Anastasia Ioannidou, Elisavet Chatzilari, Spiros Nikolopoulos, Ioannis Yiannis Kompatsiaris · 2017 · ACM Computing Surveys
62. Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas
    Niha G. Beig, Mohammadhadi Khorrami, Mehdi Alilou, Prateek Prasanna, Nathaniel M. Braman, Mahdi Orooji, Sagar Rakshit, Kaustav Bera, Prabhakar Shantha Rajiah, J Ginsberg et al. · 2018 · Radiology
63. Load Forecasting Techniques for Power System: Research Challenges and Survey
    Naqash Ahmad, Yazeed Yasin Ghadi, Muhammad Adnan, Mansoor Ali · 2022 · IEEE Access
64. A computer vision approach for automated analysis and classification of microstructural image data
    Brian DeCost, Elizabeth A. Holm · 2015 · Computational Materials Science
65. Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
    Hanna Geppert, Martin Vogt, Jürgen Bajorath · 2010 · Journal of Chemical Information and Modeling
66. Artificial Intelligence Techniques for Stability Analysis and Control in Smart Grids: Methodologies, Applications, Challenges and Future Directions
    Shi, Zhongtuo, Yao, Wei, Li, Zhouping, Zeng, Lingkang, Zhao, Yifan, Zhang, Runfeng, Tang, Yong, Wen, Jinyu · 2020 · Applied Energy
67. Traffic sign recognition — How far are we from the solution?
    Markus Mathias, Radu Timofte, Rodrigo Benenson, Luc Van Gool · 2013 · International Joint Conference on Neural Networks (IJCNN)
68. A tutorial on support vector machine-based methods for classification problems in chemometrics
    Jan Luts, Fabian Ojeda, Raf Van de Plas, Bart De Moor, Sabine Van Huffel, Johan A. K. Suykens · 2010 · Analytica Chimica Acta
69. Support vector machine for regression and applications to financial forecasting
    Theodore B. Trafali̇s, Hüseyin İnce · 2000 · International Joint Conference on Neural Networks (IJCNN)
70. Fault Diagnosis of an Autonomous Vehicle With an Improved SVM Algorithm Subject to Unbalanced Datasets
    Qian Shi, Hui Zhang · 2020 · IEEE Transactions on Industrial Electronics
71. Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review
    Ayoub Benali Amjoud, Mustapha Amrouch · 2023 · IEEE Access
72. - The Physical Principles of Remote Sensing
    Keith R. McCloy · 2005 · CRC Press eBooks
73. Human pluripotent stem cell-derived neural constructs for predicting neural toxicity
    Schwartz, Michael P., Hou, Zhonggang, Propson, Nicholas E., Zhang, Jue, Engstrom, Collin J., Costa, Vitor Santos, Jiang, Peng, Nguyen, Bao Kim, Bolin, Jennifer M., Daly, William et al. · 2015 · Proceedings of the National Academy of Sciences
74. “Predicting MOOC dropout over weeks using machine learning methods,”
    Kloft, Marius, Stiehler, Felix, Zheng, Zhilin, Pinkwart, Niels · 2014 · Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs
75. Machine learning for power system disturbance and cyber-attack discrimination
    Raymond Borges Hink, Justin M. Beaver, Mark A. Buckner, Tommy Morris, Uttam Adhikari, Shengyi Pan · 2014 · International Symposium on Resilient Control Systems (ISRCS)
76. Learning the Kernel with Hyperkernels
    Cheng Soon Ong, Alexander Johannes Smola, Robert Charles Williamson, Ralf Herbrich · 2003 · ANU Open Research (Australian National University)
77. Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison
    Alejandro González, Zhijie Fang, Yainuvis Socarras, Joan Serrat, David Vázquez, Jiaolong Xu, Antonio Manuel López · 2016 · Sensors
78. Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
    Tianyue Zheng, Weihong Deng, Jiani Hu · 2017 · arXiv (Cornell University)
79. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)
    Lehmann, Christoph, Koenig, Thomas, Jelic, Vesna, Prichep, Leslie, John, Roy E., Wahlund, Lars-Olof, Dodge, Yadolah, Dierks, Thomas · 2007 · Journal of Neuroscience Methods
80. Support Vector Machine Classification Algorithm and Its Application
    Yongli Zhang · 2012 · Communications in computer and information science
81. An algorithmic theory of learning: Robust concepts and random projection
    Rosa I. Arriaga, Santosh Vempala · 2006 · Machine Learning
82. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning
    Xiaofeng Zhu, Yaorong Ge, Taoran Li, Danthai Thongphiew, F.-F. Yin, Qiuwen Wu · 2011 · Medical Physics
83. Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
    Zhi-Ce Fang, Yi Wang, Ling Peng, H. Hong · 2020 · Computational Geosciences
84. Review of numerical optimization techniques for meta-device design [Invited]
    Campbell, Sawyer D., Sell, David, Jenkins, Ronald P., Whiting, Eric B., Fan, Jonathan A., Werner, Douglas H. · 2019 · Optical Materials Express
85. Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability
    Paulino José García Nieto, Esperanza García–Gonzalo, Fernando Sánchez Lasheras, Francisco Javier de Cos Juez · 2015 · Reliability Engineering & System Safety
86. Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online
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87. Detection of early plant stress responses in hyperspectral images
    Jan Behmann, Jörg Steinrücken, Lutz Plümer · 2014 · ISPRS Journal of Photogrammetry and Remote Sensing
88. BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches
    Bin Liu · 2017 · Briefings in Bioinformatics
89. Applications of functional data analysis: A systematic review
    Shahid Ullah, Caroline F. Finch · 2013 · BMC Medical Research Methodology
90. Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure
    Zufan Zhang, Hao Luo, Chun Wang, Chenquan Gan, Yong Xiang · 2020 · IEEE Transactions on Vehicular Technology
91. Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System
    Prabhakar Sharma, Zafar Said, Anurag Kumar, Sandro Nižetić, Ashok Pandey, Anh Tuan Hoang, Zuohua Huang, Asif Afzal, Changhe Li, Lê Anh Tuấn et al. · 2022 · Energy & Fuels
92. Experiments with Random Projection
    Sanjoy Dasgupta · 2013 · arXiv (Cornell University)
93. Large-scale Learning with SVM and Convolutional for Generic Object Categorization
    Fu Jie Huang, Yann LeCun · 2006 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
94. Foundations of Data Science
    Avrim L. Blum, John E. Hopcroft, Ravindran Kannan · 2020 · Cambridge University Press eBooks
95. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting
    Cong Feng, Mingjian Cui, Bri‐Mathias S. Hodge, Jie Zhang · 2017 · Applied Energy
96. Utilizing support vector machine in real-time crash risk evaluation
    Rongjie Yu, Mohamed A. Abdel-Aty · 2012 · Accident Analysis & Prevention
97. Support Vector Machines Under Adversarial Label Noise
    Battista Biggio, Blaine A. Nelson, Pavel Laskov · 2011 · UNICA IRIS Institutional Research Information System (University of Cagliari)
98. A comparative review on sleep stage classification methods in patients and healthy individuals
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99. Machine learning applications in minerals processing: A review
    John T. McCoy, Lidia Auret · 2018 · Minerals Engineering
100. Artificial intelligence and machine learning in spine research
     Fabio Galbusera, Gloria Casaroli, Tito Bassani · 2019 · JOR Spine