Support-vector networks

Cited by (1,253)

Items 401–500 of 1,253

1. Local Directional Number Pattern for Face Analysis: Face and Expression Recognition
   Adıń Ramıŕez Rivera, Jorge Rojas Castillo, Oksam Chae · 2012 · IEEE Transactions on Image Processing
2. Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures
   Ran Nathan, Orr Spiegel, Scott Fortmann‐Roe, Roi Harel, Martin C. Wikelski, Wayne Marcus Getz · 2012 · Journal of Experimental Biology
3. Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review
   Sushil Lamichhane, Lalit Kumar, Brian R. Wilson · 2019 · Geoderma
4. Text categorization with support vector machines
   Thorsten Joachims · 1997 · Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
5. Wavelet Support Vector Machine
   Lifu Zhang, Wen Hao Zhou, Licheng Jiao · 2004 · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
6. An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis
   Raymond J. Langley, Ephraim L. Tsalik, Jennifer C. van Velkinburgh, Seth W. Glickman, Brandon Rice, Chunping Wang, Bo Chen, Lawrence Carin, Arturo Suarez, Robert P. Mohney et al. · 2013 · Science Translational Medicine
7. Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
   Wei Yu, Tiebin Liu, Rodolfo A. Valdez, MARTA L. GWINN, Muin J. Khoury · 2010 · BMC Medical Informatics and Decision Making
8. Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
   Robin Winter, Floriane Montanari, Frank Noé, Djork-Arné Clevert · 2018 · Chemical Science
9. Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach
   Yang Bao, Bin Ke, Bin Li, Yingri Julia Yu, Jie Zhang · 2019 · Journal of Accounting Research
10. Machine Learning Methods for Small Data Challenges in Molecular Science
    Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, Lu Ke, Long Chen, Jiang Jian, Yueying Zhu, Jie Liu, Bengong Zhang, Guo‐Wei Wei · 2023 · Chemical Reviews
11. Domain Adaptation: Learning Bounds and Algorithms
    Mansour, Yishay, Mohri, Mehryar, Rostamizadeh, Afshin · 2009 · arXiv
12. The application of in silico drug-likeness predictions in pharmaceutical research
    Sheng Tian, Junmei Wang, Youyong Li, Dan Li, Lei Xu, Tingjun Hou · 2015 · Advanced Drug Delivery Reviews
13. An Equivalence Between Sparse Approximation and Support Vector Machines
    Federico Girosi · 1998 · Neural Computation
14. Mapping the landscape of Artificial Intelligence applications against COVID-19
    Joseph Aylett-Bullock, Alexandra Sasha Luccioni, Katherine Hoffman Pham, Cynthia Sin Nga Lam, Miguel Angel Luengo-Oroz · 2020 · Journal of Artificial Intelligence Research
15. Classification-Reconstruction Learning for Open-Set Recognition
    Ryota Yoshihashi, Wen Quan Shao, Rei Kawakami, Shaodi You, Makoto Iida, Takeshi Naemura · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
16. Stealing Hyperparameters in Machine Learning
    Binghui Wang, Neil Zhenqiang Gong · 2018 · IEEE Symposium on Security and Privacy (S&P)
17. FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
    Ciftci, Umur Aybars, Demir, Ilke, Yin, Lijun · 2024 · IEEE Transactions on Pattern Analysis and Machine Intelligence
18. Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT
    Yutong Xie, Yong Xia, Jianpeng Zhang, Yang Song, Dagan D. Feng, MICHAEL J. FULHAM, Weidong Cai · 2018 · IEEE Transactions on Medical Imaging
19. A Survey on Text Classification: From Traditional to Deep Learning
    Qian Li, Hao Ming Peng, Jianxin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He · 2022 · ACM Transactions on Intelligent Systems and Technology
20. Face recognition based on image sets
    Hakan Çevıkalp, Bill Triggs · 2010 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
21. A category-free neural population supports evolving demands during decision-making
    David Chillón Raposo, Matthew Tyler Kaufman, Anne K. Churchland · 2014 · Nature Neuroscience
22. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
    Faming Huang, Zhongshan Cao, Jianfei Guo, Shui‐Hua Jiang, Shu Li, Zizheng Guo · 2020 · CATENA
23. The Transporter Classification Database
    Milton H. Saier, Vamsee S. Reddy, Dorjee G. Tamang, Åke Västermark · 2013 · Nucleic Acids Research
24. Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
    Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis‐Philippe Morency · 2019 · Proceedings of the AAAI Conference on Artificial Intelligence
25. Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
    Yun Wang, Bongjin Seo, Bowen Wang, Nada Zamel, Kui Jiao, Xavier Cordobes Adroher · 2020 · Energy and AI
26. Domain Adaptive Neural Networks for Object Recognition
    Muhammad Ghifary, Willem Bastiaan Kleijn, Mengjie Zhang · 2014 · Lecture notes in computer science
27. A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
    Ervin Čeperić, Vladimir Čeperić, Adrijan Barić · 2013 · IEEE Transactions on Power Systems
28. Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
    Daniel Asante Otchere, Tarek Omar Arbi Ganat, Raoof Gholami, Syahrir Ridha · 2020 · Journal of Petroleum Science and Engineering
29. “Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content
    Molina, Maria D., Sundar, S. Shyam, Le, Thai, Lee, Dongwon · 2021 · American Behavioral Scientist
30. Supervised classification of human microbiota
    Dan Knights, Elizabeth K. Costello, Rob D. Knight · 2010 · FEMS Microbiology Reviews
31. An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
    Goo Jun, Mary Kate Wing, Gonçalo R. Abecasis, Hyun Min Kang · 2015 · Genome Research
32. Using functional domain composition and support vector machines for prediction of protein subcellular location
    Chou, Kuo-Chen, Cai, Yu-Dong · 2002 · Journal of Biological Chemistry
33. A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series
    Seçkin Karasu, Aytaç Altan, Stelios D. Bekiros, Wasim Ahmad · 2020 · Energy
34. A tutorial on ν ‐support vector machines
    Pai-Hsuen Chen, Chih‐Jen Lin, Bernhard Schölkopf · 2005 · Applied Stochastic Models in Business and Industry
35. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm
    Hongze Li, Sen Guo, Chunjie Li, Jingqi Sun · 2012 · Knowledge-Based Systems
36. Constructing support vector machine ensemble
    Hyun‐Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, Sung Yang Bang · 2003 · Pattern Recognition
37. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study
    Vijay R. Varma, Anup Mammen Oommen, Sudhir Varma, Ramon Casanova, Yang An, Ryan M. Andrews, Richard O’Brien, Olga Pletniková, Juan Carlos Troncoso, Jon B. Toledo et al. · 2018 · PLoS Medicine
38. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge
    Weiyi Sun, Anna Rumshisky, Özlem Uzuner · 2013 · Journal of the American Medical Informatics Association
39. Support Vector Machines – An Introduction
    Vojislav Kecman · 2005 · Studies in fuzziness and soft computing
40. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
    Paul A. Viola, Michael Jones · 2001
41. Machine Learning in Additive Manufacturing: A Review
    Lingbin Meng, Brandon McWilliams, William Jarosinski, Hyeyoung Park, Yeon‐Gil Jung, Je-Hyun Lee, Jing Zhang · 2020 · JOM
42. Automated Crack Detection on Concrete Bridges
    Prateek Prasanna, Kristin Dana, Nenad Gucunski, Basily Basily, Hung Manh La, Ronny Salim Lim, Hooman Parvardeh · 2014 · IEEE Transactions on Automation Science and Engineering
43. What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle
    Guang-Bin Huang · 2015 · Cognitive Computation
44. Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI
    Benoît Magnin, Lilia Mesrob, Serge Kinkingnéhun, Mélanie Pélégrini‐Issac, Olivier Colliot, Marie Sarazin, Bruno Dubois, Stéphane Lehéricy, Habib Benali · 2008 · Neuroradiology
45. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine
    Chenghai Xue, Fei Li, Tao He, Guo-Ping Liu, Yanda Li, Xuegong Zhang · 2005 · BMC Bioinformatics
46. Learning Locally-Adaptive Decision Functions for Person Verification
    Zhen Li, Shiyu Chang, Feng Liang, Thomas S. Huang, Liangliang Cao, John R. Smith · 2013 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
47. On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification
    Jorge Manuel Santos, Mark J. Embrechts · 2009 · Lecture notes in computer science
48. Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis
    Wei Li, Saurabh Prasad, James E. Fowler, L.M. Bruce · 2011 · IEEE Transactions on Geoscience and Remote Sensing
49. Modelling and mapping soil organic carbon stocks in Brazil
    Lucas Carvalho Gomes, Raíza Moniz Faria, Eliana de Souza, Gustavo Vieira Veloso, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho · 2019 · Geoderma
50. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method
    Mahyat Shafapour Tehrany, Biswajeet Pradhan, Mustafa Neamah Jebur · 2015 · Stochastic Environmental Research and Risk Assessment
51. A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
    Hong‐Tzer Yang, Chao‐Ming Huang, Yann-Chang Huang, Yi-Shiang Pai · 2014 · IEEE Transactions on Sustainable Energy
52. GAN Inversion: A Survey
    Weihao Xia, Yulun Zhang, Yujiu Yang, Jing‐Hao Xue, Bolei Zhou, Ming–Hsuan Yang · 2022 · IEEE Transactions on Pattern Analysis and Machine Intelligence
53. A review on neural networks with random weights
    Weipeng Cao, Xizhao Wang, Zhong Ming, Jinzhu Gao · 2017 · Neurocomputing
54. A Review on a Deep Learning Perspective in Brain Cancer Classification
    Gopal Singh Tandel, Mainak Biswas, Omprakash G. Kakde, Ashish Tiwari, Harman S. Suri, Monica Turk, John R. Laird, Christopher K Asare, Annabel A. Ankrah, Narendra Nath Khanna et al. · 2019 · Cancers
55. Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review
    Ernest Yeboah Boateng, Joseph Otoo, Daniel A. Abaye · 2020 · Journal of Data Analysis and Information Processing
56. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
    Lei Chen, Yu-Hang Zhang, Shaopeng Wang, Yunhua Zhang, Tao Huang, Yu‐Dong Cai · 2017 · PLoS ONE
57. Drones count wildlife more accurately and precisely than humans
    Jarrod C. Hodgson, Rowan Mott, Shane M. Baylis, Trung Thanh Pham, Simon J. Wotherspoon, Adam D. Kilpatrick, Ramesh Raja Segaran, Ian D. Reid, Aleks Terauds, Lian Pin Koh · 2018 · Methods in Ecology and Evolution
58. Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
    Ling-Ling Li, Xue Zhao, Ming‐Lang Tseng, Raymond R. Tan · 2019 · Journal of Cleaner Production
59. A structural approach to relaxation in glassy liquids
    Samuel S. Schoenholz, Ekin D. Cubuk, Daniel M. Sussman, Efthimios Kaxiras, Andrea J. Liu · 2016 · Nature Physics
60. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
    Yin-Wen Chang, Cho‐Jui Hsieh, Kai‐Wei Chang, Michael Ringgaard, Chih‐Jen Lin · 2010
61. A survey on face detection in the wild: Past, present and future
    Stefanos P. Zafeiriou, Cha Zhang, Zheng-You Zhang · 2015 · Computer Vision and Image Understanding
62. A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization
    Linqiang Pan, Cheng He, Ye Tian, Handing Wang, Xingyi Zhang, Yaochu Jin · 2018 · IEEE Transactions on Evolutionary Computation
63. Machine Learning Methods in Drug Discovery
    Patel, Lauv, Shukla, Tripti, Huang, Xiuzhen, Ussery, David W., Wang, Shanzhi · 2020 · Molecules
64. Empirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers
    Vladimir I. Koltchinskii, Dmitry Panchenko · 2002 · The Annals of Statistics
65. TSVR: An efficient Twin Support Vector Machine for regression
    Xinjun Peng · 2009 · Neural Networks
66. Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
    Yaochu Jin, Bernhard Sendhoff · 2008 · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
67. A Guide to Convolutional Neural Networks for Computer Vision
    Salman Hameed Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun · 2018 · Synthesis lectures on computer vision
68. A review of deep learning with special emphasis on architectures, applications and recent trends
    Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, Sayak Paul, Vasilios A. Tsalavoutis, Frederick Ditliac Atiah, Vadlamani Ravi, Alan Peters · 2020 · Knowledge-Based Systems
69. SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification
    Manuel Tardáguila, Lorena de la Fuente, Cristina Martí, Cécile Pereira, Francisco J. Pardo-Palacios, Héctor del Risco, Marc Ferrell, Maravillas Mellado-López, Marissa G. Macchietto, Kenneth Verheggen et al. · 2018 · Genome Research
70. A review of channel selection algorithms for EEG signal processing
    Turky N. Alotaiby, Fathi E. Abd El‐Samie, Saleh A. Alshebeili, Ishtiaq Ahmad · 2015 · EURASIP Journal on Advances in Signal Processing
71. Evaluating the State-of-the-Art in Automatic De-identification
    Özlem Uzuner, Yuan Luo, Peter Szolovits B · 2007 · Journal of the American Medical Informatics Association
72. Machine Learning Paradigms for Speech Recognition: An Overview
    Li Deng, Xiao Li · 2013 · IEEE Transactions on Audio Speech and Language Processing
73. Predicting the Band Gaps of Inorganic Solids by Machine Learning
    Ya Zhuo, Aria Mansouri Tehrani, Jakoah Brgoch · 2018 · The Journal of Physical Chemistry Letters
74. Application of multivariate statistical techniques in microbial ecology
    Oleg Paliy, Vijay Shankar · 2016 · Molecular Ecology
75. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network
    Chen, Junwen, Liu, Zhigang, Wang, Hongrui, Nunez, Alfredo, Han, Zhiwei · 2018 · IEEE Transactions on Instrumentation and Measurement
76. An Introduction to Boosting and Leveraging
    Ron Meir, Gunnar Rätsch · 2003 · Lecture notes in computer science
77. Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
    Wouter G. Touw, Jumamurat R. Bayjanov, Lex Overmars, L. Backus, Jos Boekhorst, Michiel Wels, Sacha A. F. T. van Hijum · 2012 · Briefings in Bioinformatics
78. A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications
    Preksha Pareek, Ankit Thakkar · 2020 · Artificial Intelligence Review
79. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers
    Elhadi Adam, Onisimo Mutanga, John Odindi, Elfatih Mohamed Abdel-Rahman · 2014 · International Journal of Remote Sensing
80. SOLAR FLARE PREDICTION USING SDO /HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM
    Monica Bobra, S. Couvidat · 2015 · The Astrophysical Journal
81. Supervised Machine Learning for Population Genetics: A New Paradigm
    Schrider, Daniel R., Kern, Andrew D. · 2018 · Trends in Genetics
82. Deep Learning in Mining Biological Data
    Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain · 2021 · Cognitive Computation
83. Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges
    Banaee, Hadi, Ahmed, Mobyen, Loutfi, Amy · 2013 · Sensors
84. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
    Xueheng Qiu, Ye Ren, Ponnuthurai Nagaratnam Suganthan, Gehan A. J. Amaratunga · 2017 · Applied Soft Computing
85. Theory of Classification: a Survey of Some Recent Advances
    Stéphane Boucheron, Olivier Bousquet, Gábor Lugosi · 2005 · ESAIM Probability and Statistics
86. An Efficient Algorithm to Identify Strong-Velocity Pulses in Multicomponent Ground Motions
    S. K. Shahi, Jack W. Baker · 2014 · Bulletin of the Seismological Society of America
87. CBSA: content-based soft annotation for multimodal image retrieval using bayes point machines
    E. Chang, King-Shy Goh, G. Sychay, Gang Wu · 2003 · IEEE Transactions on Circuits and Systems for Video Technology
88. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
    An Dinh, Stacey Miertschin, Amber Young, Somya D. Mohanty · 2019 · BMC Medical Informatics and Decision Making
89. Change-point detection in time-series data by relative density-ratio estimation
    Song Liu, Makoto Yamada, Nigel Collier, Masashi Sugiyama · 2013 · Neural Networks
90. Deep Representation Learning With Part Loss for Person Re-Identification
    Hantao Yao, Shiliang Steven Zhang, Richang Hong, Yongdong Zhang, Changsheng Xu, Qi Tian · 2019 · IEEE Transactions on Image Processing
91. Selecting appropriate machine learning methods for digital soil mapping
    Yones Khaledian, Bradley A. Miller · 2019 · Applied Mathematical Modelling
92. Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods
    Ekin D. Cubuk, Samuel S. Schoenholz, Jennifer M. Rieser, Brad D. Malone, Jörg Rottler, Douglas J. Durian, Efthimios Kaxiras, Andrea J. Liu · 2015 · Physical Review Letters
93. Graph kernels for chemical informatics
    Liva Ralaivola, S. Joshua Swamidass, Hiroto Saigo, Pierre Baldi · 2005 · Neural Networks
94. Distributed Intrusion Detection System in a Multi-Layer Network Architecture of Smart Grids
    Yichi Zhang, Lingfeng Wang, Weiqing Sun, Robert Green, Mansoor Alam · 2011 · IEEE Transactions on Smart Grid
95. Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption
    Yaozhong Wu, Jianguang Fang, Chi Wu, Cunyi Li, Guangyong Sun, Qing Li · 2023 · International Journal of Mechanical Sciences
96. Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines
    Jawad Nagi, Keem Siah Yap, Tiong Sieh Kiong, Syed Khaleel Ahmed, Malik Mohamad · 2009 · IEEE Transactions on Power Delivery
97. Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.
    Christian Gobert, Edward W. Reutzel, Jan Petrich, Abdalla R. Nassar, Shashi Phoha · 2018 · Additive manufacturing
98. Content-based audio classification and retrieval by support vector machines
    Guodong Guo, S.Z. Li · 2003 · IEEE Transactions on Neural Networks
99. A hybrid machine learning approach to network anomaly detection
    Taeshik Shon, Jongsub Moon · 2007 · Information Sciences
100. Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities
     Meng Shen, Xiangyun Tang, Liehuang Zhu, Xiaojiang Du, Mohsen Guizani · 2019 · IEEE Internet of Things Journal