Support-vector networks

Cited by (1,253)

Items 501–600 of 1,253

1. Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review
   Lei Li, Shuming Rong, Rui Wang, Shuili Yu · 2020 · Chemical Engineering Journal
2. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests
   João Paulo Marôco, Dina Silva, Ana Pina Rodrigues, Manuela Guerreiro, Isabel J. Santana, Alexandre de Mendonça · 2011 · BMC Research Notes
3. An up-to-date comparison of state-of-the-art classification algorithms
   Zhang, Chongsheng, Liu, Changchang, Zhang, Xiangliang, Almpanidis, George · 2017 · Expert Systems with Applications
4. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data
   Martin N. Hebart, Kai Görgen, John­–Dylan Haynes · 2015 · Frontiers in Neuroinformatics
5. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
   Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li · 2020 · Landslides
6. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers
   Gavin C. Cawley, Nicola L. C. Talbot · 2003 · Pattern Recognition
7. A support vector machine-based ensemble algorithm for breast cancer diagnosis
   Haifeng Wang, Bichen Zheng, Sang Won Yoon, Hoo Sang Ko · 2017 · European Journal of Operational Research
8. Classification With Ant Colony Optimization
   David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens · 2007 · IEEE Transactions on Evolutionary Computation
9. From genome-wide arrays to tailor-made biomarker readout – Progress towards routine analysis of skin sensitizing chemicals with GARD
   Andy Forreryd, Kathrin Stephanie Zeller, Tim Lindberg, Henrik Johansson, Malin Lindstedt · 2016 · Toxicology in Vitro
10. Machine Learning and Decision Support in Critical Care
    Alistair E. W. Johnson, Mohammad M. Ghassemi, Shamim Nemati, Katherine E. Niehaus, David Andrew Clifton, Gari D. Clifford · 2016 · Proceedings of the IEEE
11. Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying
    Karthik Dinakar, Birago Jones, Catherine Havasi, Henry A. Lieberman, Rosalind W. Picard · 2012 · ACM Transactions on Interactive Intelligent Systems
12. Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach
    Lei Lin, Zhengbing He, Srinivas Peeta · 2018 · Transportation Research Part C Emerging Technologies
13. A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks
    Vaia I. Kontopoulou, Athanasios D. Panagopoulos, Iοannis Kakkos, George K. Matsopoulos · 2023 · Future Internet
14. Discriminating Data
    Wendy Hui Kyong Chun, Alex Barnett · 2021 · The MIT Press eBooks
15. A deep look into radiomics
    Scapicchio, Camilla, Gabelloni, Michela, Barucci, Andrea, Cioni, Dania, Saba, Luca, Neri, Emanuele · 2021 · La radiologia medica
16. Describable Visual Attributes for Face Verification and Image Search
    Neeraj Kumar, Alex R. Berg, Peter N. Belhumeur, Shree K. Nayar · 2011 · IEEE Transactions on Pattern Analysis and Machine Intelligence
17. Global food security – Issues, challenges and technological solutions
    Ultan McCarthy, Ismail Uysal, Ricardo Badia-Melis, Samuel Mercier, Colm P. O’Donnell, Anastasia Ktenioudaki · 2018 · Trends in Food Science & Technology
18. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning
    Zhixiong Li, Ziyang Zhang, Junchuan Shi, Dazhong Wu · 2019 · Robotics and Computer-Integrated Manufacturing
19. A review of epileptic seizure detection using machine learning classifiers
    Mohammad Khubeb Siddiqui, Rubén Morales-Menéndez, Xiaodi Huang, Nasir Hussain · 2020 · Brain Informatics
20. Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks
    Thilina, Karaputugala Madushan, Kae Won Choi, Saquib, Nazmus, Hossain, Ekram · 2013 · IEEE Journal on Selected Areas in Communications
21. Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques
    Kristof Coussement, Dirk Van den Poel · 2006 · Expert Systems with Applications
22. Hyperspectral Image Classification With Independent Component Discriminant Analysis
    Alberto Villa, Jón Atli Benediktsson, Jocelyn Chanussot, Christian Jutten B · 2011 · IEEE Transactions on Geoscience and Remote Sensing
23. A review of machine learning in processing remote sensing data for mineral exploration
    Hojat Shirmard, Ehsan Farahbakhsh, R. Dietmar Müller, Rohitash Chandra · 2021 · Remote Sensing of Environment
24. Margin based feature selection - theory and algorithms
    Ran Gilad-Bachrach, Amir Navot, Naftali Tishby · 2004 · International Conference on Machine Learning
25. Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
    Anthony J. Bagnall, Jason Lines, Jon Hills, Aaron George Bostrom · 2015 · IEEE Transactions on Knowledge and Data Engineering
26. Face recognition with support vector machines: global versus component-based approach
    Bernd Heisele, Purdy Ho, Tomaso Poggio · 2002 · IEEE/CVF International Conference on Computer Vision (ICCV)
27. Carbon price forecasting based on CEEMDAN and LSTM
    Feite Zhou, Zhehao Huang, Changhong Zhang · 2022 · Applied Energy
28. A review on machine learning principles for multi-view biological data integration
    Yifeng Li, Fang‐Xiang Wu, Alioune Ngom · 2016 · Briefings in Bioinformatics
29. A comparative study of machine learning classifiers for modeling travel mode choice
    Hagenauer, Julian, Helbich, Marco · 2017 · Expert Systems with Applications
30. Support vector machines for hyperspectral remote sensing classification
    J. Anthony Gualtieri, Robert F. Cromp · 1999 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
31. Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification
    Indu Jain, Vinod Kumar Jain, Renu Jain · 2017 · Applied Soft Computing
32. Deep learning and machine vision for food processing: A survey
    Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos N. Plataniotis · 2021 · Current Research in Food Science
33. Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction
    Maciej Zięba, Sebastian Klaudiusz Tomczak, Jakub M. Tomczak · 2016 · Expert Systems with Applications
34. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems
    Tanzeel U. Rehman, Md Sultan Mahmud, Young Ki Chang, Jian Jin, Jaemyung Shin · 2018 · Computers and Electronics in Agriculture
35. Engineering support vector machine kernels that recognize translation initiation sites
    Alexander Zien, Gunnar Rätsch, S. Mika, Bernhard Schölkopf, Thomas Lengauer, K. Robert Müller · 2000 · Bioinformatics
36. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment
    Laura Judith Marcos-Zambrano, Kanita Karađuzović-Hadžiabdić, Tatjana Lončar-Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland, Aleksandra Gruca, Jasminka Hasić Telalović, Karel Hron et al. · 2021 · Frontiers in Microbiology
37. Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses
    Evandro De Barros Costa, Baldoíno Fonseca, Marcelo Almeida Santana, Fabrísia Ferreira de Araújo, Joilson B. A. Rego · 2017 · Computers in Human Behavior
38. Selecting training sets for support vector machines: a review
    Jakub Nalepa, Michał Kawulok · 2018 · Artificial Intelligence Review
39. iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
    Zhen Chen, Pei Zhao, Fuyi Li, Tatiana T. Marquez‐Lago, André Leier, Jerico Revote, Yan Li Zhu, David Powell, Tatsuya Akutsu, Geoffrey I. Webb et al. · 2019 · Briefings in Bioinformatics
40. Autonomous agents modelling other agents: A comprehensive survey and open problems
    Albrecht, Stefano V., Stone, Peter · 2018 · Artificial Intelligence
41. Linear Programming Boosting via Column Generation
    Ayhan Demiriz, Kristin P. Bennett, John S. Shawe-Taylor · 2002 · Machine Learning
42. Faecalibacterium prausnitzii subspecies-level dysbiosis in the human gut microbiome underlying atopic dermatitis
    Song, Han, Yoo, Young, Hwang, Junghyun, Na, Yun-Cheol, Kim, Heenam Stanley · 2016 · Journal of Allergy and Clinical Immunology
43. Statistical and machine learning models in credit scoring: A systematic literature survey
    Xolani Dastile, Turgay Çelik, Moshe Moses Potsane · 2020 · Applied Soft Computing
44. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
    Julia Ling, Jeremy Alan Templeton · 2015 · Physics of Fluids
45. Characterization of four-class motor imagery EEG data for the BCI-competition 2005
    Alois Schlögl, Felix Lee, Horst Bischof, Gert Pfurtscheller · 2005 · Journal of Neural Engineering
46. Machine Learning for Detection and Diagnosis of Disease
    Paul Sajda · 2006 · Annual Review of Biomedical Engineering
47. Remote sensing for wetland classification: a comprehensive review
    Sahel Mahdavi, Bahram Salehi, Jean Elizabeth Granger, Meisam Amani, Brian Brisco, Weimin Huang · 2017 · GIScience & Remote Sensing
48. A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
    Thomas Wiatowski, Helmut Bölcskei · 2017 · IEEE Transactions on Information Theory
49. Centroid-Based Document Classification: Analysis and Experimental Results
    Eui-Hong Sam Han, George Karypis · 2000 · Lecture notes in computer science
50. FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
    Rukshan Batuwita, Vasile Palade · 2010 · IEEE Transactions on Fuzzy Systems
51. Deep learning for denoising
    Siwei Yu, Jianwei Ma, Wenlong Wang · 2019 · Geophysics
52. The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM
    Giles M. Foody, Ajay Mathur · 2006 · Remote Sensing of Environment
53. Support Vector Machines for Automated Gait Classification
    Rezaul K. Begg, Marimuthu Swami Palaniswami, Brendan Owen · 2005 · IEEE Transactions on Biomedical Engineering
54. Collecting and Analyzing Multidimensional Data with Local Differential Privacy
    Ning Wang, Xiaokui Xiao, Yin Yang, Jun Zhao, Siu Cheung Hui, Hyejin Shin, Junbum Shin, Ge Yu · 2019 · IEEE International Conference on Data Engineering (ICDE)
55. Deep Learning Recommendation Model for Personalization and Recommendation Systems
    Naumov, Maxim, Mudigere, Dheevatsa, Shi, Hao-Jun Michael, Huang, Jianyu, Sundaraman, Narayanan, Park, Jongsoo, Wang, Xiaodong, Gupta, Udit, Wu, Carole-Jean, Azzolini, Alisson G. et al. · 2019 · arXiv
56. Parsing Argumentation Structures in Persuasive Essays
    Christian Stab, Iryna Gurevych · 2017 · Computational Linguistics
57. Argument Mining: A Survey
    John Shelton Lawrence, Chris Reed · 2019 · Computational Linguistics
58. Computational assignment of cell-cycle stage from single-cell transcriptome data
    Antonio Scialdone, Kedar Nath Natarajan, Luís R. Saraiva, Valentina Proserpio, Sarah Amalia Teichmann, Oliver Stegle, John C. Marioni, Florian Buettner · 2015 · Methods
59. Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets
    Ming-Ju Wu, Jyh‐Shing Roger Jang, Jui-Long Chen · 2014 · IEEE Transactions on Semiconductor Manufacturing
60. Predicting alzheimer’s disease progression using multi-modal deep learning approach
    Lee, Garam, Nho, Kwangsik, Kang, Byungkon, Sohn, Kyung-Ah, Kim, Dokyoon, for Alzheimer’s Disease Neuroimaging Initiative, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R. et al. · 2019 · Scientific Reports
61. Short-Term Load Forecasting Based on an Adaptive Hybrid Method
    Shuai Fan, L.‐W. Antony Chen · 2006 · IEEE Transactions on Power Systems
62. A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
    Frederic E. Bock, Roland Can Aydin, Christian Johannes Cyron, Norbert W. Huber, Surya R. Kalidindi, Benjamin Klusemann · 2019 · Frontiers in Materials
63. An algorithmic perspective on imitation learning
    Osa, Takayuki, Pajarinen, Joni, Neumann, Gerhard, Bagnell, J. Andrew, Abbeel, Pieter, Peters, Jan · 2018 · Foundations and Trends in Robotics
64. Multiclass Support Vector Machines for EEG-Signals Classification
    İnan Güler, Elif Derya Übeylı · 2007 · IEEE Transactions on Information Technology in Biomedicine
65. Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population
    Chris Hinrichs A, Vikas Singh, Guofan Xu, Sterling C. Johnson · 2010 · NeuroImage
66. A clinical text classification paradigm using weak supervision and deep representation
    Yanshan Wang, Sunghwan Sohn, Sijia Liu, Feichen Shen, Liwei Wang, Elizabeth J. Atkinson, Shreyasee Amin, Hongfang Liu · 2019 · BMC Medical Informatics and Decision Making
67. A survey on graph kernels
    Nils M. Kriege, Fredrik Johansson, Christopher Morris · 2020 · Applied Network Science
68. What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine
    Jakub Kufel, Katarzyna Bargieł-Łączek, Szymon Kocot, Maciej Koźlik, Wiktoria Bartnikowska, Michał Janik, Łukasz Czogalik, Piotr Dudek, Mikołaj Magiera, Anna Lis et al. · 2023 · Diagnostics
69. Comparing automated text classification methods
    Hartmann, Jochen, Huppertz, Juliana, Schamp, Christina, Heitmann, Mark · 2019 · International Journal of Research in Marketing
70. Time series analysis and long short-term memory neural network to predict landslide displacement
    Beibei Yang, Kunlong Yin, Suzanne Lacasse, Zhongqiang Liu · 2019 · Landslides
71. The Mathematics of Learning: Dealing With Data
    Steve Smale · 2005 · International Conference on Neural Networks and Brain
72. Risk assessment in social lending via random forests
    Milad Malekipirbazari, Vural Aksakallı · 2015 · Expert Systems with Applications
73. Deep neural network concepts for background subtraction:A systematic review and comparative evaluation
    Thierry Bouwmans, Sajid Javed, Maryam Sultana, Soon Ki Jung · 2019 · Neural Networks
74. A survey of MRI-based brain tumor segmentation methods
    Jin Liu, Min Li, Jianxin Wang, Fang‐Xiang Wu, Tianming Liu, Yi Feng Pan · 2014 · Tsinghua Science & Technology
75. The Operational Value of Social Media Information
    Ruomeng Cui, Santiago Gallino, Antonio Moreno, Dennis Zhang · 2015 · Production and Operations Management
76. Dropout prediction in e-learning courses through the combination of machine learning techniques
    Ioanna Lykourentzou, Ioannis Giannoukos, Vassilis Nikolopoulos, George Mpardis, Vassili G. Loumos · 2009 · Computers & Education
77. Topic sentiment analysis in twitter
    Xiaolong Wang, Furu Wei, Xiaohua Liu, Ming Xing Zhou, Ming Zhang · 2011 · ACM International Conference on Information and Knowledge Management (CIKM)
78. An Algorithmic Perspective on Imitation Learning
    Takayuki Osa, Joni Pajarinen, Gerhard M. Neumann, James Andrew Bagnell, Pieter Abbeel, Jan Peters · 2018 · now publishers, Inc. eBooks
79. Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
    Sohrab Zendehboudi, Nima Rezaei, Ali Lohi · 2018 · Applied Energy
80. Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
    Henry Friday Nweke, Teh Ying Wah, Ghulam Mujtaba, Mohammed Ali Al-Garadi · 2018 · Information Fusion
81. Ensemble deep learning for regression and time series forecasting
    Xueheng Qiu, Le Zhang, Ye Ren, Ponnuthurai Nagaratnam Suganthan, Gehan A. J. Amaratunga · 2014
82. A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models
    Abdullah Al Mamun, Md. Sohel, Naeem Mohammad, Md Samiul Haque Sunny, Debopriya Roy Dipta, Eklas Hossain · 2020 · IEEE Access
83. Face recognition: component-based versus global approaches
    Bernd Heisele, Purdy Ho, Jane Wu, Tomaso Poggio · 2003 · Computer Vision and Image Understanding
84. SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
    Marcos Zampieri, Preslav Nakov, Sara Rosenthal, Pepa Atanasova, Georgi Karadzhov, Hamdy Mubarak, Leon Derczynski, Zeses Pitenis, Çağrı Çöltekin · 2020 · International Workshop on Semantic Evaluation (SemEval)
85. The choice of scaling technique matters for classification performance
    Lucas Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz · 2022 · Applied Soft Computing
86. Algorithms for Learning Kernels Based on Centered Alignment
    Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh · 2012 · arXiv (Cornell University)
87. Identification of Hybrid Systems A Tutorial
    Simone Paoletti, Aleksandar Lj. Juloski, Giancarlo Ferrari-trecate A, René Vidal · 2007 · European Journal of Control
88. Boosting image retrieval
    Kinh H. Tieu, Paul A. Viola · 2002 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
89. Application of machine learning methods to spatial interpolation of environmental variables
    Jin Li, Andrew D. Heap, Anna Potter, James J. Daniell · 2011 · Environmental Modelling & Software
90. State-of-the-Art Deep Learning in Cardiovascular Image Analysis
    Geert J. S. Litjens, Francesco Ciompi, Jelmer M. Wolterink, Bob D. de Vos, Tim Leiner, Jonas Teuwen, Ivana Išgum · 2019 · JACC. Cardiovascular imaging
91. Robustness and generalization
    Huan Xu, Shie Mannor · 2011 · Machine Learning
92. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
    Alberto Fernández, Victoria López, Mikel Galar, María José del Jesús, Francisco Herrera · 2013 · Knowledge-Based Systems
93. Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
    Di Zhang, Junqiang Lin, Qidong Peng, Dongsheng Wang, Tiantian Yang, Soroosh Sorooshian, Xuefei Liu, Jiangbo Zhuang · 2018 · Journal of Hydrology
94. An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review
    Rosita Guido, Stefania Ferrisi, Danilo Lofaro, Domenico Conforti · 2024 · Information
95. Large-scale image classification: Fast feature extraction and SVM training
    Yuanqing Lin, Fengjun Lv, Shenghuo Zhu B, Shuicheng Yan, Timothée Cour, Kai Yu, Liangliang Cao, Thomas S. Huang · 2011 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
96. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine
    Maitiniyazi Maimaitijiang, Abduwasit Ghulam, Sidike Paheding, Sean Hartling, Matthew Maimaitiyiming, Kyle T. Peterson, Ethan J. Shavers, Jack Fishman, Jim Peterson, Suhas Kadam et al. · 2017 · ISPRS Journal of Photogrammetry and Remote Sensing
97. Understanding Emotions in Text Using Deep Learning and Big Data
    Ankush Chatterjee, Umang Gupta, Manoj Kumar Chinnakotla, Radhakrishnan Srikanth, Michel Galley, Puneet Agrawal · 2018 · Computers in Human Behavior
98. Band Selection for Hyperspectral Image Classification Using Mutual Information
    B. Guo, S.R. Gunn, R.I. Damper, James D. B. Nelson · 2006 · IEEE Geoscience and Remote Sensing Letters
99. Automatic detection of cyberbullying in social media text
    Cynthia Van Hee, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter M. P. Daelemans, Véronique Hoste · 2018 · PLoS ONE
100. The CHIME Fast Radio Burst Project: System Overview
     Mandana Amiri, Kevin Bandura, Philippe Berger, Mohit Bhardwaj, Michelle M. Boyce, P. J. Boyle, Charanjot Brar, Maya Burhanpurkar, Pragya Chawla, Jaydeep Ghosh Chowdhury et al. · 2018 · The Astrophysical Journal