Support-vector networks

Cited by (1,253)

Items 801–900 of 1,253

1. Text Mining in Big Data Analytics
   Hossein Hassani, Christina C. Beneki, Stephan Unger, Maedeh Taj Mazinani, Mohammad Reza Yeganegi · 2020 · Big Data and Cognitive Computing
2. Learning by Transduction
   Alex Gammerman, Vladimir Vovk, Vladimir N. Vapnik · 2013 · arXiv (Cornell University)
3. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar
   Miao Liu, Mingjun Wang, Jun Wang, Duo Li · 2012 · Sensors and Actuators B Chemical
4. Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
   Victoria López, Alberto Fernández, Jose G. Moreno-Torres, Francisco Herrera · 2011 · Expert Systems with Applications
5. Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity
   Sherali Zeadally, Erwin Adi, Zubair Ahmed Baig, Imran Ali Khan · 2020 · IEEE Access
6. Machine learning in construction: From shallow to deep learning
   Yayin Xu, Ying Zhou, Przemysław Sekuła, Lieyun Ding · 2021 · Developments in the Built Environment
7. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines
   Tarek Lajnef, Sahbi Chaibi, Perrine Marie Ruby, Pierre‐Emmanuel Aguera, Jean‐Baptiste Eichenlaub, Mounir Samet, Abdennaceur Kachouri, Karim Jerbi · 2015 · Journal of Neuroscience Methods
8. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
   S. Sathiya Keerthi · 2002 · IEEE Transactions on Neural Networks
9. Application of machine learning in intelligent fish aquaculture: A review
   Shili Zhao, Song Zhang, Jincun Liu, He Wang, Zhu Jia, Daoliang Li, Ran Zhao · 2021 · Aquaculture
10. Deep learning techniques to classify agricultural crops through UAV imagery: a review
    Abdelmalek Bouguettaya, Hafed Zarzour, Ahmed Kechida, Amine Mohammed Taberkit · 2022 · Neural Computing and Applications
11. Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
    Haoyu Zhang, Quan Zou, Ying Ju, Chenggang Song, Dong Chen · 2022 · Current Bioinformatics
12. A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization
    Nour, Majid, Cömert, Zafer, Polat, Kemal · 2020 · Applied Soft Computing
13. Improving the Accuracy and Speed of Support Vector Machines
    Christopher J. C. Burges, Bernhard Schölkopf · 1996
14. An evaluation of statistical spam filtering techniques
    Le Zhang, Jingbo Zhu, Tianshun Yao · 2004 · ACM Transactions on Asian Language Information Processing
15. Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning
    Hyunjun Kim, Eunjong Ahn, Myoungsu Shin, Sung‐Han Sim · 2018 · Structural Health Monitoring
16. Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
    Koutsoukas, Alexios, Monaghan, Keith J., Li, Xiaoli, Huan, Jun · 2017 · Journal of Cheminformatics
17. Using support vector machine models for crash injury severity analysis
    Zhibin Li, Pan Liu, Wei Wang, Chengcheng Xu · 2011 · Accident Analysis & Prevention
18. On the application of machine learning techniques to derive seismic fragility curves
    Jalal Kiani, Charles V. Camp, Shahram Pezeshk · 2019 · Computers & Structures
19. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
    Schulz, Marc-Andre, Yeo, B. T. Thomas, Vogelstein, Joshua T., Mourao-Miranada, Janaina, Kather, Jakob N., Kording, Konrad, Richards, Blake, Bzdok, Danilo · 2020 · Nature Communications
20. Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
    Xibin Zhou, Chao Chen, Zhanchao Li, Xiaoyong Zou · 2007 · Journal of Theoretical Biology
21. Fast and Accurate Time Series Classification with WEASEL
    Patrick Schäfer, Ulf Leser · 2017 · ACM International Conference on Information and Knowledge Management (CIKM)
22. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
    Marta P. Fernandes, Susana Margarida Vieira, Francisca Leite, Carlos Palos, Stan N. Finkelstein, João M. C. Sousa · 2019 · Artificial Intelligence in Medicine
23. Remote Sensing Object Detection in the Deep Learning Era—A Review
    Shengxi Gui, Shuang Song, Rongjun Qin, Yang Tang · 2024 · Remote Sensing
24. Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images
    Mamadou Dian Bah, Adel Hafiane, Raphaël Canals · 2018 · Remote Sensing
25. Retinal layer segmentation of macular OCT images using boundary classification
    Andrew Lang, Aaron Carass, Matthew Hauser, Elias S. Sotirchos, Peter A. Calabresi, Howard Shann-Cherng Ying, Jerry L. Prince · 2013 · Biomedical Optics Express
26. A survey on incorporating domain knowledge into deep learning for medical image analysis
    Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu · 2021 · Medical Image Analysis
27. Machine Learning in Materials Science
    Tim Mueller, Aaron Gilad Kusne, Rampi Ramprasad · 2016 · Reviews in computational chemistry
28. A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses
    Yondo, Raul, Andrés, Esther, Valero, Eusebio · 2018 · Progress in Aerospace Sciences
29. Machine learning in side-channel analysis: a first study
    Gabriel Hospodar, Benedikt Gierlichs, Elke De Mulder, Ingrid M.R. Verbauwhede, Joos P. L. Vandewalle · 2011 · Journal of Cryptographic Engineering
30. Cell death stages in single apoptotic and necrotic cells monitored by Raman microspectroscopy
    Eva Brauchle, Sibylle Thude, Sara Yvonne Brucker, Katja Schenke‐Layland · 2014 · Scientific Reports
31. A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification
    Sourish Ghosh, Anasuya Dasgupta, Aleena Swetapadma · 2019 · International Conference on Intelligent Sustainable Systems (ICISS)
32. MLbase: A Distributed Machine-learning System
    Tim Kraska, Ameet Talwalkar, John C. Duchi · 2013
33. Time Series Classification With Multivariate Convolutional Neural Network
    Chien-Liang Liu, W. Hsaio, Yao-Chung Tu · 2019 · IEEE transactions on industrial electronics (1982. Print)
34. Review of Classification Methods on Unbalanced Data Sets
    Le Wang, Meng Han, Xiaojuan Li, Ni Zhang, Haodong Cheng · 2021 · IEEE Access
35. Nested cross-validation when selecting classifiers is overzealous for most practical applications
    Wainer, Jacques, Cawley, Gavin · 2021 · Expert Systems with Applications
36. A gentle introduction to deep learning for graphs
    Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda · 2020 · Neural Networks
37. Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning
    Benjamin Karg, Sergio Lucia · 2020 · IEEE Transactions on Cybernetics
38. A machine learning framework for sport result prediction
    Rory Bunker, Fadi Thabtah · 2017 · Applied Computing and Informatics
39. Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning
    Madalina-Mihaela Buzau, Javier Tejedor-Aguilera, Pedro Cruz-Romero, Antonio Gómez‐Expósito · 2018 · IEEE Transactions on Smart Grid
40. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study
    Njoud AlMansour, Hajra F. Syed, Nuha Radwan Khayat, Rawan Al-Theeb, Renad Emad Juri, Jamal A. Alhiyafi, Saleh Alrashed, Sunday Olusanya Olatunji · 2019 · Computers in Biology and Medicine
41. A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification
    Xinqiao Zhao, Hongmiao Zhang, Guilin Zhu, Fengxiang You, Shaolong Kuang, Lining Sun · 2019 · IEEE Transactions on Neural Systems and Rehabilitation Engineering
42. Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
    Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike, Ahmad M. Daloye, H. Erkbol, Felix Fritschi · 2020 · Remote Sensing
43. Object detection using a max-margin Hough transform
    Subhransu Maji, Jitendra Malik · 2009 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
44. Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis
    Wenbin Yue, Zidong Wang, Hongwei Chen, Annette M. Payne, Xiaohui Liu · 2018 · Designs
45. Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning
    Xiang Li, Zhanli Liu, Shaoqing Cui, Chengcheng Luo, Chenfeng Li, Zhuo Zhuang · 2019 · Computer Methods in Applied Mechanics and Engineering
46. Rice plant disease classification using color features: a machine learning paradigm
    Vimal Kumar Shrivastava, Monoj K. Pradhan · 2020 · Journal of Plant Pathology
47. Revealing household characteristics from smart meter data
    Christian Beckel, Leyna Sadamori, Thorsten Staake, Silvia Santini B · 2014 · Energy
48. Quantitative Ultrasound in Soft Tissues
    Jonathan Mamou, Michael L. Oelze · 2013 · Springer eBooks
49. Multivariate lesion-symptom mapping using support vector regression
    Yong-Sheng Zhang, Daniel Y. Kimberg, H. Branch Coslett, Myrna F. Schwartz, Ze Wang · 2014 · Human Brain Mapping
50. Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data
    Anthony T.C. Goh, S.H. Goh · 2007 · Computers and Geotechnics
51. Analysis of microarray experiments of gene expression profiling
    Adi Laurentiu Tarca, Roberto J. Romero, Sorin Drăghici · 2006 · American Journal of Obstetrics and Gynecology
52. Ensemble feature selection: Homogeneous and heterogeneous approaches
    Borja Seijo-Pardo, Iago Porto-Díaz, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos · 2016 · Knowledge-Based Systems
53. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors
    Li, Frédéric, Shirahama, Kimiaki, Nisar, Muhammad, Köping, Lukas, Grzegorzek, Marcin · 2018 · Sensors
54. A Survey of Applications and Human Motion Recognition with Microsoft Kinect
    Roanna Lun, Wenbing Zhao · 2015 · International Journal of Pattern Recognition and Artificial Intelligence
55. A Pervasive Approach to EEG‐Based Depression Detection
    Hanshu Cai, Jiashuo Han, Yun-Fei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen et al. · 2018 · Complexity
56. Comprehensive review on twin support vector machines
    M. Tanveer, T. Rajani, Reshma Rastogi, Yuan‐Hai Shao, M. A. Ganaie · 2022 · Annals of Operations Research
57. Recipe recommendation using ingredient networks
    Chun-Yuen Teng, Yu‐Ru Lin, Lada A. Adamic · 2012 · ACM Web Science Conference (WebSci)
58. Feature extraction and recognition of ictal EEG using EMD and SVM
    Shufang Li, Weidong Zhou, Qi Yuan, Shujuan Geng, Dongmei Cai · 2013 · Computers in Biology and Medicine
59. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification
    Luis H. S. Vogado, Rodrigo De Melo Souza Veras, Flávio H. D. Araújo, Romuere Silva, Kelson Romulo Teixeira Aires · 2018 · Engineering Applications of Artificial Intelligence
60. A Robust Deep Model for Improved Classification of AD/MCI Patients
    Feng Li, Loc Tran, Kim‐Han Thung, Shuiwang Ji, Dinggang G. Shen, Li Jiang · 2015 · IEEE Journal of Biomedical and Health Informatics
61. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data
    Xuegong Zhang, Xin L. Lu, Qian Shi, Xiuqin Xu, Hon‐Chiu Eastwood Leung, Lyndsay N. Harris, James Dirk Iglehart, Alexander Miron, Jun S. Liu, Wing Hung Wong · 2006 · BMC Bioinformatics
62. Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images
    Long Wang, Zi-Jun Zhang · 2017 · IEEE transactions on industrial electronics (1982. Print)
63. Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges
    Ravil I. Mukhamediev, Yelena Popova, Yan I. Kuchin, Elena Zaitseva, Almas M. Kalimoldayev, Адилхан Сымагулов, Vitaly Levashenko, Farida Abdoldina, Viktors I. Gopejenko, Kirill Yakunin et al. · 2022 · Mathematics
64. On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion
    Alexander Johannes Smola, Bernhard Schölkopf · 1998 · Algorithmica
65. A Machine Learning Approach to Predict Air Quality in California
    Mauro Castelli, Fabiana Martins Clemente, Aleš Popovič, Sara Silva, Leonardo Vanneschi · 2020 · Complexity
66. A physics-informed deep learning approach for bearing fault detection
    Sheng Shen, Hao Lü, Mohammadkazem Sadoughi, Chao Kun Hu, Venkat Pavan Nemani, Adam Thelen, Keith Webster, Matthew J. Darr, Jeff Sidon, Shawn Kenny · 2021 · Engineering Applications of Artificial Intelligence
67. Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction
    Laxmi Narayan Sharma, Rajesh Kumar Tripathy, Samarendra Dandapat · 2015 · IEEE Transactions on Biomedical Engineering
68. Prior Knowledge in Support Vector Kernels
    Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir N. Vapnik · 1998
69. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping
    Zhice Fang, Yi Wang, Ling Peng, Haoyuan Hong · 2020 · International Journal of Geographical Information Systems
70. Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework
    Liu, Wenbo, Li, Ming, Yi, Li · 2016 · Autism Research
71. Apnea medassist: Real-time sleep apnea monitor using single-lead ECG
    Bsoul, Majdi, Minn, Hlaing, Tamil, Lakshman · 2011 · IEEE Transactions on Information Technology in Biomedicine
72. Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications
    Luxton, David D. · 2014 · Professional Psychology Research and Practice
73. Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture
    Michael Fernández, Peter G. Boyd, Thomas D. Daff, Mohammad Zein Aghaji, Tom K. Woo · 2014 · The Journal of Physical Chemistry Letters
74. Fundamentals of natural computing: an overview
    L DECASTRO · 2006 · Physics of Life Reviews
75. Review on deep learning applications in frequency analysis and control of modern power system
    Yi Zhang, Xiaohan Shi, Hengxu Zhang, Yongji Cao, Vladimir V. Terzija · 2021 · International Journal of Electrical Power & Energy Systems
76. A systematic performance evaluation of clustering methods for single-cell RNA-seq data
    Duò, Angelo, Robinson, Mark D., Soneson, Charlotte · 2018 · F1000Research
77. Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation
    O. Devos, Cyril Ruckebusch, Alexandra Durand, Ludovic Duponchel, Jean‐Pierre Huvenne · 2008 · Chemometrics and Intelligent Laboratory Systems
78. Cytoskeleton-based forecasting of stem cell lineage fates
    Matthew D. Treiser, Eric Yang, Simon Gordonov, Daniel M. Cohen, Ioannis P. Androulakis, Joachim Kohn, Christopher S. Chen, Prabhas V. Moghe · 2009 · Proceedings of the National Academy of Sciences
79. Road Extraction Using SVM and Image Segmentation
    Mingjun Song, Daniel L. Civco · 2004 · Photogrammetric Engineering & Remote Sensing
80. Support Vector Machines with Applications
    Javier M. Moguerza, Alberto Muñoz · 2006 · Statistical Science
81. A review on distance based time series classification
    Amaia Abanda, Usue Mori, Jose Antonio Lozano · 2018 · Data Mining and Knowledge Discovery
82. Recommendations as Treatments: Debiasing Learning and Evaluation
    Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims · 2016 · arXiv (Cornell University)
83. Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity
    Anderson, Blake, McGrew, David · 2017 · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
84. GraphProt: modeling binding preferences of RNA-binding proteins
    Daniel Maticzka, Sita Lange, Fabrizio Costa, R. Backofen · 2014 · Genome Biology
85. High-Dimensional Cluster Analysis with the Masked EM Algorithm
    Shabnam Kadir, Dan F. M. Goodman, Kenneth D. Harris · 2014 · Neural Computation
86. Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT
    Cong Shi, Jian Liu, Hongbo Liu, Yingying Jennifer Chen · 2017 · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
87. Digital image analysis in breast pathology—from image processing techniques to artificial intelligence
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88. Predicting property prices with machine learning algorithms
    Winky K.O. Ho, Bo Tang, Siu Wai Wong · 2020 · Journal of Property Research
89. Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review
    Aaruththiran Manoharan, Mumtaj Begam, Vimal Rau Aparow, Denesh Sooriamoorthy · 2022 · Journal of Energy Storage
90. Multi-time scale stream flow predictions: The support vector machines approach
    Tirusew Asefa, Mariush Kemblowski, Mac McKee, Abedalrazq F. Khalil · 2005 · Journal of Hydrology
91. Automated Polyp Detection in Colon Capsule Endoscopy
    Alexander V. Mamonov, Isabel N. Figueiredo, Pedro Figueiredo, Yen‐Hsi Richard Tsai · 2014 · IEEE Transactions on Medical Imaging
92. A Winnow-Based Approach to Context-Sensitive Spelling Correction
    Andrew R. Golding, Dan Roth · 1999 · Machine Learning
93. Urban Air Pollution Monitoring System With Forecasting Models
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94. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
    Faming Huang, Jinsong Huang, Shui‐Hua Jiang, Chuangbing Zhou · 2017 · Engineering Geology
95. NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
    Alessandro Aimar, Hesham Mostafa, Enrico Calabrese, Antonio Ríos-Navarro, Ricardo Tapiador-Morales, Iulia-Alexandra Lungu, Moritz B. Milde, Federico Corradi, Alejandro Linares-Barranco, Shih‐Chii Liu et al. · 2018 · IEEE Transactions on Neural Networks and Learning Systems
96. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques
    Bahram Choubin, Hamid R. Darabi, Omid Rahmati, Farzaneh Sajedi Hosseini, Bjørn Kløve · 2017 · The Science of The Total Environment
97. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers
    Víctor Manuel Mondéjar-Guerra, Jorge Novo, José Rouco, Manuel G. Penedo, Marcos Ortega · 2018 · Biomedical Signal Processing and Control
98. Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals
    Vipin Gupta, Mayur Dahyabhai Chopda, Ram Bilas Pachori · 2018 · IEEE Sensors Journal
99. Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining
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100. Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors
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