Support-vector networks

Cited by (1,254)

Items 901–1,000 of 1,254

1. Robust twin support vector machine for pattern classification
   Zhiquan Qi, Yingjie Tian, Yong Shi · 2012 · Pattern Recognition
2. Predicting Malignant Nodules from Screening CT Scans
   Samuel H. Hawkins, Hua Wang, Ying Liu, Alberto López García, Olya Stringfield, Henry Krewer, Qian Li, Dmitry Cherezov, Robert A. Gatenby, Yoganand Balagurunathan et al. · 2016 · Journal of Thoracic Oncology
3. Machine learning in geo- and environmental sciences: From small to large scale
   Pejman Tahmasebi, Serveh Kamrava, Tao Bai, Muhammad Sahimi · 2020 · Advances in Water Resources
4. Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt
   Jun Jie Yuan, Tao Wen, He Zhang, Mengli Zhao, C. Ryan Penton, Linda S. Thomashow, Qirong Shen · 2020 · The ISME Journal
5. Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions
   Masoud Aliramezani, Charles Robert Koch, Mahdi Shahbakhti · 2021 · Progress in Energy and Combustion Science
6. Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features
   Sana Ullah Jan, Young-Doo Lee, Jungpil Shin, Insoo Koo · 2017 · IEEE Access
7. Machine learning for wireless communications in the Internet of Things: A comprehensive survey
   Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, Tommaso Melodia · 2019 · Ad Hoc Networks
8. Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
   Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, Fabio Roli · 2017 · IEEE Transactions on Dependable and Secure Computing
9. Machine learning techniques for code smell detection: A systematic literature review and meta-analysis
   Muhammad Ilyas Azeem, Fabio Palomba, Lin Shi, Qing Wang · 2019 · Information and Software Technology
10. DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires
    John-William Sidhom, H. Benjamin Larman, Drew M. Pardoll, Alexander Spyridon Baras · 2021 · Nature Communications
11. Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV
    Ghulab Nabi Ahmad, Hira Fatima, Shafiullah, Abdelaziz Salah Saidi, A Imdadullah · 2022 · IEEE Access
12. Noninvasive Monitoring of Blood Glucose with Raman Spectroscopy
    Pandey, Rishikesh, Paidi, Santosh Kumar, Valdez, Tulio A., Zhang, Chi, Spegazzini, Nicolas, Dasari, Ramachandra Rao, Barman, Ishan · 2017 · Accounts of Chemical Research
13. Theory and Algorithms
    Peter Schlattmann · 2008 · Statistics in the health sciences
14. Classification of small UAVs and birds by micro-Doppler signatures
    Pavlo Molchanov, Ronny I. A. Harmanny, Jaco J.M. de Wit, Karen Egiazarian, Jaakko T. Astola · 2014 · International Journal of Microwave and Wireless Technologies
15. Daily streamflow prediction using optimally pruned extreme learning machine
    Rana Muhammad Adnan, Zhongmin Liang, Slaviša Trajković, Mohammad Zounemat‐Kermani, Binquan Li, Ozgur Kisi · 2019 · Journal of Hydrology
16. A Review of Machine Learning for Near-Infrared Spectroscopy
    Wenwen Zhang, Chamara Kasun Liyanaarachchi Lekamalage, Qi Jie Wang, Yuanjin Zheng, Zhiping Lin · 2022 · Sensors
17. DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling
    Haipeng Cai, Na Meng, Barbara G. Ryder, Daphne Yao · 2018 · IEEE Transactions on Information Forensics and Security
18. Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design
    Aanchna Sharma, Tanmoy Mukhopadhyay, Sanjay Mavinkere Rangappa, Suchart Siengchin, Vinod Kushvaha · 2022 · Archives of Computational Methods in Engineering
19. Deep Clustering: A Comprehensive Survey
    Ya-Zhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiao-Rong Pu, Philip S. Yu, Lifang He · 2022 · IEEE Transactions on Neural Networks and Learning Systems
20. Kernel Methods and Machine Learning
    Sun‐Yuan Kung · 2014 · Cambridge University Press eBooks
21. Boosting support vector machines for imbalanced data sets
    Benjamin X. Wang, Nathalie Japkowicz · 2009 · Knowledge and Information Systems
22. An optimized XGBoost method for predicting reservoir porosity using petrophysical logs
    Shaowei Pan, Zechen Zheng, Zhi Guo, Haining Luo · 2021 · Journal of Petroleum Science and Engineering
23. Does learning require memorization? a short tale about a long tail
    Feldman, Vitaly · 2020 · Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing
24. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model
    Wei‐Chiang Hong · 2008 · Energy Conversion and Management
25. Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms
    Kshyanaprava Panda Panigrahi, Himansu Das, Abhaya Kumar Sahoo, Suresh Chandra Moharana · 2020 · Advances in intelligent systems and computing
26. Recent advances in surface defect inspection of industrial products using deep learning techniques
    Zheng, Xiaoqing, Zheng, Song, Kong, Yaguang, Chen, Jie · 2021 · The International Journal of Advanced Manufacturing Technology
27. Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient
    Guancen Lin, Aijing Lin, Danlei Gu · 2022 · Information Sciences
28. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines
    Fabian Löw, Ulrich Michel, Stefan W. Dech, Christopher Conrad · 2013 · ISPRS Journal of Photogrammetry and Remote Sensing
29. Learning Non-Linear Combinations of Kernels
    Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh · 2009
30. Support Vector Machines Applications
    Yunqian Ma, Guodong Guo · 2014 · Springer eBooks
31. Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT
    Yutong Xie, Jianpeng Zhang, Yong Xia, MICHAEL J. FULHAM, Yanning Zhang · 2017 · Information Fusion
32. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression
    Jing Xiao, Ruifeng Ding, Xiulin Xu, Haochen Guan, Xinhui Feng, Tao Sun, Sibo Zhu, Zhibin Ye · 2019 · Journal of Translational Medicine
33. Use of support vector learning for chunk identification
    Taku Kudoh, Yūji Matsumoto · 2000 · Conference on Computational Natural Language Learning
34. Incorporating invariances in support vector learning machines
    Bernhard Schölkopf, Chris J. C. Burges, Vladimir N. Vapnik · 1996 · Lecture notes in computer science
35. Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals
    Abhijit Bhattacharyya, Ram Bilas Pachori, Abhay B. Upadhyay, U. Rajendra Acharya · 2017 · Applied Sciences
36. Deep support vector machine for hyperspectral image classification
    Onuwa Okwuashi, Christopher E. Ndehedehe · 2020 · Pattern Recognition
37. Support vector machines for predicting membrane protein types by using functional domain composition
    Cai, Yu-Dong, Zhou, Guo-Ping, Chou, Kuo-Chen · 2003 · Biophysical Journal
38. Automatic breast ultrasound image segmentation: a survey
    Xian, Min, Zhang, Yingtao, Cheng, H.D., Xu, Fei, Zhang, Boyu, Ding, Jianrui · 2018 · Pattern Recognition
39. Improvements to the Percolator Algorithm for Peptide Identification from Shotgun Proteomics Data Sets
    Marina Spivak, Jason Weston, Léon Bottou, Lukas Käll, William Stafford Noble · 2009 · Journal of Proteome Research
40. A comparison of models for predicting early hospital readmissions
    Joseph Futoma, Jonathan D. Morris, Joseph E. Lucas · 2015 · Journal of Biomedical Informatics
41. The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis
    Yue Hou, Qiuhan Li, Chen Zhang, Guoyang Lu, Zhoujing Ye, Yihan Chen, Linbing Wang, Dandan Cao · 2020 · Engineering
42. Robust support vector method for hyperspectral data classification and knowledge discovery
    Gustau Camps‐Valls, Luis Gómez‐Chova, Javier Calpe‐Maravilla, José D. Martín‐Guerrero, Emilio Soria‐Olivas, Luis Alonso, J. Moreno · 2004 · IEEE Transactions on Geoscience and Remote Sensing
43. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models
    Huanhuan Yuan, Guijun Yang, Changchun Li, Yanjie Wang, Jiangang Liu, Haiyang Yu, Haikuan Feng, Bo Xu, Xiaoqing Zhao, Xiaodong Yang · 2017 · Remote Sensing
44. How to pre-process Raman spectra for reliable and stable models?
    Bocklitz, Thomas, Walter, Angela, Hartmann, Katharina, Rösch, Petra, Popp, Jürgen · 2011 · Analytica Chimica Acta
45. Distributional word clusters vs. words for text categorization
    Ron Bekkerman, Ran El‐Yaniv, Naftali Tishby, Yoad Winter · 2003
46. A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms
    Baha Şen, Musa Peker, Abdullah Çavuşoğlu, Fatih V. Çelebi · 2014 · Journal of Medical Systems
47. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
    Robin Wolz, Valtteri Julkunen, Juha R. Koikkalainen, Eini I. Niskanen, Dong Ping Zhang, Daniel Rueckert, Hilkka S. Soininen, Jyrki M. P. Lötjönen, the Alzheimer's Disease Neuroimaging Initiative · 2011 · PLoS ONE
48. Boosting Image Retrieval
    Kinh H. Tieu, Paul A. Viola · 2003 · International Journal of Computer Vision
49. Extraction of Spectral Information from Hyperspectral Data and Application of Hyperspectral Imaging for Food and Agricultural Products
    Lankapalli Ravikanth, Digvir Singh Jayas, Noel D.G. White, Paul G. Fields, Da‐Wen Sun · 2016 · Food and Bioprocess Technology
50. Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review
    Siti-Farhana Lokman, Abu Talib Othman, Muhammad-Husaini Abu-Bakar · 2019 · EURASIP Journal on Wireless Communications and Networking
51. Information retrieval and text mining technologies for chemistry
    Krallinger, Martin, Rabal, Obdulia, Lourenço, Anália, Oyarzabal, Julen, Valencia, Alfonso · 2017 · Chemical Reviews
52. A systematic study on the application of scatter-corrective and spectral-derivative preprocessing for multivariate prediction of soil organic carbon by Vis-NIR spectra
    André Carnieletto Dotto, Ricardo Simão Diniz Dalmolin, Alexandre ten Caten, Sabine Grunwald · 2017 · Geoderma
53. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
    Hongbin Yang, Lixia Sun, Weihua Li, Guixia Liu, Yun Tang · 2018 · Frontiers in Chemistry
54. An AIoT Based Smart Agricultural System for Pests Detection
    Ching‐Ju Chen, Ya-Yu Huang, Yuan-Shuo Li, Chuan‐Yu Chang, Yueh‐Min Huang · 2020 · IEEE Access
55. DianNao
    Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, Olivier Temam · 2014 · ACM SIGPLAN Notices
56. Polarization and Fake News: Early Warning of Potential Misinformation Targets
    Vicario, Michela Del, Quattrociocchi, Walter, Scala, Antonio, Zollo, Fabiana · 2019 · ACM Transactions on the Web
57. Prediction of droughts over Pakistan using machine learning algorithms
    Najeebullah Khan, Dhanapala Arachchige Sachindra, Shamsuddin Shahid, Kamal Ahmed, Mohammed Sanusi Shiru, Nadeem Nawaz · 2020 · Advances in Water Resources
58. Hybrid convolutional neural networks with SVM classifier for classification of skin cancer
    Duggani Keerthana, Vipin Venugopal, Malaya Kumar Nath, Madhusudhan Mishra · 2022 · Biomedical Engineering Advances
59. Aggregate features and ADABOOST for music classification
    James Bergstra, Norman Casagrande, Dumitru Erhan, Douglas Eck, Balázs Kégl · 2006 · Machine Learning
60. Predicting short-term stock prices using ensemble methods and online data sources
    Bin Weng, Lin Lü, Xing Wang, Fadel M. Megahed, Waldyn G. Martinez · 2018 · Expert Systems with Applications
61. iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
    Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yongzi Chen, Tatsuya Akutsu, Roger J. Daly, Geoffrey I. Webb, Quanzhi Zhao et al. · 2021 · Nucleic Acids Research
62. Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions
    Nusrat Rouf, Majid Bashir Malik, Tasleem Arif, Sparsh Sharma, Saurabh Singh, Satyabrata Aich, Hee‐Cheol Kim · 2021 · Electronics
63. A support vector regression model for predicting tunnel boring machine penetration rates
    Satar Mahdevari, Kourosh Shahriar, Saffet Yağız, Mohsen Akbarpour Shirazi · 2014 · International Journal of Rock Mechanics and Mining Sciences
64. Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation
    Shuang Li, Shiji Song, Gao Ming Huang, Zhengming Ding, Cheng Wu · 2018 · IEEE Transactions on Image Processing
65. Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System
    Keping Yu, Long Lin, Mamoun Alazab, Liang Tan, Bo Gu · 2020 · IEEE Transactions on Intelligent Transportation Systems
66. Antibiotics degradation by advanced oxidation process (AOPs): Recent advances in ecotoxicity and antibiotic-resistance genes induction of degradation products
    Shuo Li, Yanan Wu, Heshan Zheng, Hongbin Li, Yongjie Zheng, Jun Nan, Jun Ma, Dillirani Nagarajan, Jo‐Shu Chang · 2022 · Chemosphere
67. Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment
    Arman Khadjeh Nassirtoussi, Saeed Reza Aghabozorgi, Teh Ying Wah, David Chek Ling Ngo · 2014 · Expert Systems with Applications
68. Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete
    Mohammad Hossein Rafiei, Waleed Hassan Khushefati, Ramazan Demirboğa, Hojjat Adeli · 2017 · ACI Materials Journal
69. Artificial intelligence in breast imaging
    Elizabeth Le, Yanzhong Wang, Yuan Huang, Sarah E. Hickman, Fiona J. Gilbert · 2019 · Clinical Radiology
70. High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms
    Tao Zhou, Yajun Geng, Jie Chen, Jianjun Pan, Dagmar Haase, Angela Lausch · 2020 · The Science of The Total Environment
71. A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges
    Jongseon Kim, Hyungjoon Kim, HyunGi Kim, Dongjun Lee, Sungroh Yoon · 2025 · Artificial Intelligence Review
72. Molecular Classification of Thyroid Nodules Using High-Dimensionality Genomic Data
    Darya I. Chudova, Jonathan I. Wilde, Eric T. Wang, Hui Wang, Nusrat Rabbee, Camila Egidio, Jessica D. Reynolds, Ed Tom, Moraima Pagan, C. Ted Rigl et al. · 2010 · The Journal of Clinical Endocrinology & Metabolism
73. PEBL
    Hwanjo Yu, Jiawei Han, Kevin Chen–Chuan Chang · 2002 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
74. LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets
    Jin Zhang, Dániel Mucs, Ulf Norinder, Fredrik Svensson · 2019 · Journal of Chemical Information and Modeling
75. Residual learning based CNN for breast cancer histopathological image classification
    Mahesh Gour, Sweta Jain, T. Sunil Kumar · 2020 · International Journal of Imaging Systems and Technology
76. Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning
    Lan Liu, Pengcheng Wang, Jun Lin, Langzhou Liu · 2020 · IEEE Access
77. Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea
    Yongeun Park, Kyung Hwa Cho, Jihwan Park, Sung Min Cha, Joon Ha Kim · 2014 · The Science of The Total Environment
78. A Survey on Fusion-Based Indoor Positioning
    Xiansheng Guo, Nirwan Ansari, Fangzi Hu, Yuan Pu Shao, Raphael E. Nkrow, Lin Li · 2019 · IEEE Communications Surveys & Tutorials
79. Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
    Jain, Ashesh, Koppula, Hema S., Raghavan, Bharad, Soh, Shane, Saxena, Ashutosh · 2015 · 2015 IEEE International Conference on Computer Vision (ICCV)
80. Towards weeds identification assistance through transfer learning
    Borja Espejo-García, Nikos Mylonas, Loukas Athanasakos, Spyros Fountas, IOANNIS B. VASILAKOGLOU · 2020 · Computers and Electronics in Agriculture
81. Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment
    Klaus Kollreider, Hartwig Fronthaler, Maycel Isaac Faraj, Josef Bigün · 2007 · IEEE Transactions on Information Forensics and Security
82. Reinforcement learning design for cancer clinical trials
    Yufan Zhao, Michael R. Kosorok, Donglin Zeng · 2009 · Statistics in Medicine
83. A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
    Abien Fred Agarap · 2018 · International Conference on Machine Learning and Computing (ICMLC)
84. Prestack and poststack inversion using a physics-guided convolutional neural network
    Reetam Biswas, Mrinal Kanti Sen, Vishal Das, Tapan Mukerji · 2019 · Interpretation
85. Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model
    Lingling Ni, Dong Wang, Jianfeng Wu, Yuankun Wang, Yuwei Tao, Jianyun Zhang, Jiufu Liu · 2020 · Journal of Hydrology
86. Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods
    Song-Shun Lin, Shui‐Long Shen, Annan Zhou, Ye‐Shuang Xu · 2020 · Automation in Construction
87. Sample Selection Bias Correction Theory
    Cortes, Corinna, Mohri, Mehryar, Riley, Michael, Rostamizadeh, Afshin · 2008 · Lecture Notes in Computer Science
88. Consistency of random survival forests
    Hemant Ishwaran, Udaya B. Kogalur · 2010 · Statistics & Probability Letters
89. Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey
    Gülcan Sarp, Mehmet Özçelik · 2016 · Journal of Taibah University for Science
90. A brief survey of machine learning methods and their sensor and IoT applications
    Uday Shankar Shanthamallu, Andreas Spanias, Cihan Tepedelenlioğlu, Mike Stanley · 2017 · International Conference on Information, Intelligence, Systems & Applications (IISA)
91. Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers
    Maniruzzaman, Md., Rahman, Md. Jahanur, Al-MehediHasan, Md., Suri, Harman S., Abedin, Md. Menhazul, El-Baz, Ayman, Suri, Jasjit S. · 2018 · Journal of Medical Systems
92. Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes
    Mohammad Hossein Rafiei, Hojjat Adeli · 2018 · Journal of Construction Engineering and Management
93. Prediction of geological conditions for a tunnel boring machine using big operational data
    Qianli Zhang, Zhenyu Liu, Jianrong Tan · 2019 · Automation in Construction
94. Combustion machine learning: Principles, progress and prospects
    Matthias Ihme, Wai Tong Chung, Aashwin Mishra · 2022 · Progress in Energy and Combustion Science
95. A comprehensive review of model compression techniques in machine learning
    Pierre V. Dantas, Waldir Sabino da Silva, L. Cordeiro, Celso B. Carvalho · 2024 · Applied intelligence (Boston)
96. Training algorithms for fuzzy support vector machines with noisy data
    Chun-Fu Lin, Sheng-de Wang · 2004 · Pattern Recognition Letters
97. Toward Intelligent Vehicular Networks: A Machine Learning Framework
    Le Liang, Hao Ye, Geoffrey Ye Li · 2018 · IEEE Internet of Things Journal
98. Indian stock market prediction using artificial neural networks on tick data
    Dharmaraja Selvamuthu, Vineet Kumar, Abhishek Mishra · 2019 · Financial Innovation
99. Text classification based on deep belief network and softmax regression
    Jiang, Mingyang, Liang, Yanchun, Feng, Xiaoyue, Fan, Xiaojing, Pei, Zhili, Xue, Yu, Guan, Renchu · 2018 · Neural Computing and Applications
100. High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
     Anton Akusok, Kaj-Mikael Björk, Yoan Miché, Amaury Lendasse · 2015 · IEEE Access