Support-vector networks

Cited by (1,253)

Items 301–400 of 1,253

1. Speaker Recognition by Machines and Humans: A tutorial review
   John H. L. Hansen, Taufiq Hasan · 2015 · IEEE Signal Processing Magazine
2. NRPSpredictor2—a web server for predicting NRPS adenylation domain specificity
   Marc Röttig, Marnix H. Medema, Kai Blin, Tilmann Weber, Christian R. Rausch, Oliver Kohlbacher · 2011 · Nucleic Acids Research
3. Mining Text Data
   Charų C. Aggarwal · 2015 · Springer eBooks
4. The connection between regularization operators and support vector kernels
   Alex J. Smola, Bernhard Schölkopf, Klaus‐Robert Müller · 1998 · Neural Networks
5. Mapping carbon accumulation potential from global natural forest regrowth
   Susan C. Cook‐Patton, Sara M. Leavitt, David A. Gibbs, Nancy L. Harris, Kristine Lister, Kristina J. Anderson‐Teixeira, Russell D. Briggs, Robin L. Chazdon, Thomas Ward Crowther, Peter Woods Ellis et al. · 2020 · Nature
6. Kernel methods for relation extraction
   Dmitry Zelenko, Chinatsu Aone, Anthony Raymond Richardella · 2002 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
7. Deep Learning and Its Applications in Biomedicine
   Chensi Cao, Feng Liu, Hai Tan, Deshou Song, Wenjie Shu, Weizhong Li, Yiming Zhou, Xiaochen Bo, Zhi Xie · 2018 · Genomics Proteomics & Bioinformatics
8. Fast Kernel Classifiers With Online And Active Learning
   Antoine Bordes, Şeyda Ertekin, Jason Weston, Léon Bottou · 2005
9. Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances
   Waleed Hilal, S. Andrew Gadsden, John Yawney · 2021 · Expert Systems with Applications
10. Deep-learning inversion: A next-generation seismic velocity model building method
    Fangshu Yang, Jianwei Ma · 2019 · Geophysics
11. Altered microRNA expression in human heart disease
    Sadakatsu Ikeda, Sek Won Kong, Jun Lü, Egbert Hubertus Bisping, Hao Zhang, Paul Denney Allen, Todd R. Golub, Burkert Mathias Pieske, William T. Pu · 2007 · Physiological Genomics
12. Learning a Distance Metric from Relative Comparisons
    Matthew Schultz, Thorsten Joachims · 2003
13. The Entire Regularization Path for the Support Vector Machine
    Trevor J. Hastie, Saharon Rosset, Robert John Tibshirani, Ji Zhu · 2004
14. Molecular similarity: a key technique in molecular informatics
    Andreas Bender, Robert Charles Glen · 2004 · Organic & Biomolecular Chemistry
15. The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
    Rathore, M. Mazhar, Shah, Syed Attique, Shukla, Dhirendra, Bentafat, Elmahdi, Bakiras, Spiridon · 2021 · IEEE Access
16. Application of support vector machine models for forecasting solar and wind energy resources: A review
    Alireza Zendehboudi, M.A. Baseer, R. Saidur · 2018 · Journal of Cleaner Production
17. Effective face frontalization in unconstrained images
    Hassner, Tal, Harel, Shai, Paz, Eran, Enbar, Roee · 2015 · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
18. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy
    Etienne Combrisson, Karim Jerbi · 2015 · Journal of Neuroscience Methods
19. Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
    Aliper, Alexander, Plis, Sergey, Artemov, Artem, Ulloa, Alvaro, Mamoshina, Polina, Zhavoronkov, Alex · 2016 · Molecular Pharmaceutics
20. A clustering technique for the identification of piecewise affine systems
    Giancarlo Ferrari-trecate A, Marco Muselli B, Diego Liberati, Manfred Morari · 2003 · Automatica
21. Metaheuristic design of feedforward neural networks: A review of two decades of research
    Varun Ojha, Ajith Abraham, Václav Snåšel · 2017 · Engineering Applications of Artificial Intelligence
22. Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers
    Md. Kamrul Hasan, Md. Ashraful Alam, Dola Das, Eklas Hossain, Mahmudul Hasan · 2020 · IEEE Access
23. Downscaling of precipitation for climate change scenarios: A support vector machine approach
    Shivam Tripathi, Vemavarapu Venkata Srinivas, Ravi Shankar Nanjundiah · 2006 · Journal of Hydrology
24. RFID Technology for IoT-Based Personal Healthcare in Smart Spaces
    Sara Amendola, Rossella Lodato, S. Manzari, Cecilia Occhiuzzi, Gaetano Marrocco · 2014 · IEEE Internet of Things Journal
25. Face recognition using kernel direct discriminant analysis algorithms
    Juwei Lu, Konstantinos N. Plataniotis, A.N. Venetsanopoulos · 2003 · IEEE Transactions on Neural Networks
26. Trends in the development of miRNA bioinformatics tools
    Liang Chen, Liisa Heikkinen, Changliang Wang, Yang� Yang, Huiyan Sun, Garry Wong · 2018 · Briefings in Bioinformatics
27. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
    Davide Chicco, Giuseppe Jurman · 2020 · BMC Medical Informatics and Decision Making
28. Learning gender with support faces
    Baback Moghaddam, Ming–Hsuan Yang · 2002 · IEEE Transactions on Pattern Analysis and Machine Intelligence
29. Machine learning and AI in marketing–Connecting computing power to human insights
    Ma, Liye, Sun, Baohong · 2020 · International Journal of Research in Marketing
30. Automatic facial expression recognition using features of salient facial patches
    S. Happy, A. Routray · 2015 · IEEE Transactions on Affective Computing
31. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
    Reza Khatami, Giorgos Mountrakis, Stephen V. Stehman · 2016 · Remote Sensing of Environment
32. Large Margin Classification Using the Perceptron Algorithm
    Yoav Freund, Robert E. Schapire · 1999 · Machine Learning
33. Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey
    Damminda Alahakoon, Xinghuo Yu · 2015 · IEEE Transactions on Industrial Informatics
34. MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers
    Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak · 2021 · Sensors
35. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
    Myszczynska, Monika A., Ojamies, Poojitha N., Lacoste, Alix M. B., Neil, Daniel, Saffari, Amir, Mead, Richard, Hautbergue, Guillaume M., Holbrook, Joanna D., Ferraiuolo, Laura · 2020 · Nature Reviews Neurology
36. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future
    Zhao, Yang, Li, Tingting, Zhang, Xuejun, Zhang, Chaobo · 2019 · Renewable and Sustainable Energy Reviews
37. Training Invariant Support Vector Machines
    Dennis DeCoste, Bernhard Schölkopf · 2002 · Machine Learning
38. Deep learning – Method overview and review of use for fruit detection and yield estimation
    Anand Koirala, Kerry Brian Walsh, Zhenglin Wang, Cheryl L. McCarthy · 2019 · Computers and Electronics in Agriculture
39. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods
    Hsuan-Tien Lin · 2005
40. Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models
    Dieu Tien Bui, Biswajeet Pradhan, Owe Löfman, Inge Revhaug · 2012 · Mathematical Problems in Engineering
41. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
    Mayr, Andreas, Klambauer, Günter, Unterthiner, Thomas, Steijaert, Marvin, Wegner, Jörg K., Ceulemans, Hugo, Clevert, Djork-Arné, Hochreiter, Sepp · 2018 · Chemical Science
42. A review of object detection based on deep learning
    Xiao, Youzi, Tian, Zhiqiang, Yu, Jiachen, Zhang, Yinshu, Liu, Shuai, Du, Shaoyi, Lan, Xuguang · 2020 · Multimedia Tools and Applications
43. Weed detection in soybean crops using ConvNets
    Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes · 2017 · Computers and Electronics in Agriculture
44. Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts
    Zhao, Zhe, Resnick, Paul, Mei, Qiaozhu · 2015 · Proceedings of the 24th International Conference on World Wide Web
45. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
    Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski · 2020 · Stochastic Environmental Research and Risk Assessment
46. Improvements on Twin Support Vector Machines
    Yuan‐Hai Shao, Chunhua Zhang, Xiaobo Wang, Nai-Yang Deng · 2011 · IEEE Transactions on Neural Networks
47. Unmasking Clever Hans predictors and assessing what machines really learn
    Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus‐Robert Müller · 2019 · RePEc: Research Papers in Economics
48. Dropout vs. batch normalization: an empirical study of their impact to deep learning
    Christian Garbin, Xingquan Zhu A, Oge Marques · 2020 · Multimedia Tools and Applications
49. A review on extreme learning machine
    Wang, Jian, Lu, Siyuan, Wang, Shui-Hua, Zhang, Yu-Dong · 2022 · Multimedia Tools and Applications
50. Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images
    S. Deitsch, Vincent Christlein, S. Berger, C. Buerhop-Lutz, A. Maier, F. Gallwitz, C. Riess · 2018 · Solar Energy
51. Breaking Cryptographic Implementations Using Deep Learning Techniques
    Houssem Maghrebi, Thibault Portigliatti, Emmanuel Prouff · 2016 · Lecture notes in computer science
52. Survey on SVM and their application in image classification
    Mayank Arya Chandra, S. S. Bedi · 2018 · International Journal of Information Technology
53. A survey on instance segmentation: state of the art
    Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat · 2020 · International Journal of Multimedia Information Retrieval
54. Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification
    Evgeny Byvatov, Uli Fechner, Jens Sadowski, Gisbert Schneider · 2003 · Journal of Chemical Information and Computer Sciences
55. Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
    Tu C. Le, V. Chandana Epa, Frank R. Burden, David Alan Winkler · 2012 · Chemical Reviews
56. Support Vector Machines: Theory and Applications
    Theodoros Evgeniou, Massimiliano Pontil · 2001 · Lecture notes in computer science
57. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
    Guomin Huang, Lifeng Wu, Xin Ma, Weiqiang Zhang, Junliang Fan, Xiang Jun Yu, Wenzhi Zeng, Hanmi Zhou · 2019 · Journal of Hydrology
58. Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
    Byeongho Heo, Minsik Lee, Sangdoo Yun, Jin Young Choi · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
59. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
    Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun · 2018 · Computers and Electronics in Agriculture
60. Neural network models and deep learning
    Nikolaus Kriegeskorte, Tal Golan · 2019 · Current Biology
61. IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
    Xiaoping Wu, Chi Zhan, Yu‐Kun Lai, Ming‐Ming Cheng, Jufeng Yang · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
62. Nanoparticle synthesis assisted by machine learning
    Huachen Tao, Tianyi Wu, Matteo Aldeghi, Tony Wu, Alán Aspuru‐Guzik, Eugenia Kumacheva · 2021 · Nature Reviews Materials
63. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation
    Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra · 2023 · Expert Systems with Applications
64. Extracting Support Data for a Given Task
    Bernhard Schölkopf, Chris J. C. Burges, Vladimir N. Vapnik · 1995
65. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms
    Bichen Zheng, Sang Won Yoon, Sarah S. Lam · 2013 · Expert Systems with Applications
66. Machine learning applications for building structural design and performance assessment: State-of-the-art review
    Han Sun, H. Burton, Honglan Huang · 2021 · Journal of Building Engineering
67. Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers
    Dai, Zhenwei, Coker, Olabisi Oluwabukola, Nakatsu, Geicho, Wu, William K. K., Zhao, Liuyang, Chen, Zigui, Chan, Francis K. L., Kristiansen, Karsten, Sung, Joseph J. Y., Wong, Sunny Hei et al. · 2018 · Microbiome
68. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI
    Lian, Chunfeng, Liu, Mingxia, Zhang, Jun, Shen, Dinggang · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
69. Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships
    Sheridan, Robert P., Wang, Wei Min, Liaw, Andy, Ma, Junshui, Gifford, Eric M. · 2016 · Journal of Chemical Information and Modeling
70. Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion
    He Huang, Fan Zhang, Hargrove, L. J., Zhi Dou, Rogers, D. R., Englehart, K. B. · 2011 · IEEE Transactions on Biomedical Engineering
71. Face recognition by support vector machines
    Guodong Guo, S.Z. Li, K.L. Chan · 2002 · International Conference on Automatic Face and Gesture Recognition
72. Support vector machines for SAR automatic target recognition
    Qun Chao Zhao, José Carlos Príncipe · 2001 · IEEE Transactions on Aerospace and Electronic Systems
73. Electrical load forecasting models: A critical systematic review
    Corentin Kuster, Yacine Rezgui, Monjur M. Mourshed · 2017 · Sustainable Cities and Society
74. Evolving support vector machines using fruit fly optimization for medical data classification
    Liming Shen, Huiling Chen, Zhe Yu, Wenchang Kang, Bingyu Zhang, Huaizhong Li, Bo Yang, Dayou Liu · 2016 · Knowledge-Based Systems
75. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks
    Benjamin Q. Huynh, Hui Li, Maryellen Lissak Giger · 2016 · Journal of medical imaging
76. Large-Scale Machine Learning with Stochastic Gradient Descent
    Léon Bottou · 2011 · Chapman & Hall/CRC series in computer science & data analysis/Series in computer science and data analysis
77. Classification of time series by shapelet transformation
    Hills, Jon, Lines, Jason, Baranauskas, Edgaras, Mapp, James, Bagnall, Anthony · 2014 · Data Mining and Knowledge Discovery
78. Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
    Jingjing Wang, Chunxiao Jiang, Haijun Zhang, Yong Ren, Kwang-Cheng Chen, Lajos Hanzo · 2020 · IEEE Communications Surveys & Tutorials
79. deepDR: a network-based deep learning approach toin silicodrug repositioning
    Xiangxiang Zeng, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, Feixiong Cheng · 2019 · Bioinformatics
80. Multi-attention Recurrent Network for Human Communication Comprehension
    Amir Zadeh, Paul Pu Liang, Soujanya Poria, Prateek Vij, Erik Cambria, Louis‐Philippe Morency · 2018 · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))
81. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)
    Bahareh Kalantar, Biswajeet Pradhan, Seyed Amir Naghibi, Alireza Motevalli, Shattri B. Mansor · 2017 · Geomatics Natural Hazards and Risk
82. Efficient machine learning models for prediction of concrete strengths
    Hoang X. Nguyen, Thanh Vu, Thuc Phuong Vo, Huu‐Tai Thai · 2020 · Construction and Building Materials
83. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach1 1Edited by B. Holland
    Sujun Hua, Zhirong Sun · 2001 · Journal of Molecular Biology
84. Kernel Methods for Deep Learning
    Youngmin Cho, Lawrence K. Saul · 2009 · eScholarship (California Digital Library)
85. A Survey on Metric Learning for Feature Vectors and Structured Data
    Aurélien Bellet, Amaury Habrard, Marc Sebban · 2013 · arXiv (Cornell University)
86. Structural risk minimization over data-dependent hierarchies
    John S. Shawe-Taylor, Peter L. Bartlett, Robert Charles Williamson, Martin Anthony · 1998 · IEEE Transactions on Information Theory
87. Chunking with support vector machines
    Taku Kudo, Yūji Matsumoto · 2001 · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
88. Machine Learning Models that Remember Too Much
    Song, Congzheng, Ristenpart, Thomas, Shmatikov, Vitaly · 2017 · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
89. Predicting defect-prone software modules using support vector machines
    Karim O. Elish, Mahmoud O. Elish · 2007 · Journal of Systems and Software
90. Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals
    Can Ye, B. V. K. Vijaya Kumar, Miguel Tavares Coimbra · 2012 · IEEE Transactions on Biomedical Engineering
91. Bagging, Boosting and the Random Subspace Method for Linear Classifiers
    Marina Skurichina, Robert P. W. Duin · 2002 · Pattern Analysis and Applications
92. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review
    Fuyong Xing, Lin Yang · 2016 · IEEE Reviews in Biomedical Engineering
93. Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
    Eiji Aramaki, Sachiko Maskawa, Mizuki Morita · 2011
94. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms
    Cheng, Jack C.P., Chen, Weiwei, Chen, Keyu, Wang, Qian · 2020 · Automation in Construction
95. A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution
    Adnan Mujahid Khan, Nasir Rajpoot, Darren E. Treanor, Derek R. Magee · 2014 · IEEE Transactions on Biomedical Engineering
96. A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques
    M. Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutiérrez, K. Kochut · 2017 · arXiv.org
97. Modeling the Detection of Textual Cyberbullying
    Karthik Dinakar, Roi Reichart, Henry A. Lieberman · 2021 · Proceedings of the International AAAI Conference on Web and Social Media
98. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images
    Pratul P. Srinivasan, Leo A. Kim, Priyatham S. Mettu, Scott W. Cousins, Grant M. Comer, Joseph A. Izatt, Sina Farsiu · 2014 · Biomedical Optics Express
99. Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories
    Siegel, Erika H., Sands, Molly K., Van den Noortgate, Wim, Condon, Paul, Chang, Yale, Dy, Jennifer, Quigley, Karen S., Barrett, Lisa Feldman · 2018 · Psychological Bulletin
100. Quality Diversity: A New Frontier for Evolutionary Computation
     Pugh, Justin K., Soros, Lisa B., Stanley, Kenneth O. · 2016 · Frontiers in Robotics and AI