Support-vector networks
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
QSVM is effective for large data, does not overfit, gives high accuracy with big feature maps, and allows use of the kernel trick.
“Among the above listed methods, QSVM is an effective computing model in case of working with a large amount of data – it does not tend to overfit, provides high accuracy when working with a big feature map, and allows use of the kernel trick [39, 40].”
From Security intrusion detection using quantum machine learning techniques (2022) · cites this paper as [39]
The paper studies classical support-vector networks, not QSVM. Section 4 describes kernel-based feature-space calculations, and Sections 6.2–6.2.2 report strong accuracy on digit datasets, including one with 60,000 training patterns; Section 6.2.1 says increasing feature-space dimensionality showed no overfitting problems in those experiments. These findings do not establish that QSVM has the stated properties or that it generally does not overfit. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSVM is becoming increasingly popular for damage identification because of its superior ability to solve nonlinear, high-dimensional and small sample problems.
“SVM is becoming increasingly popular these years for damage identification because of its superior ability to solve nonlinear, high-dimensional and small sample problems [173].”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [173]
The Abstract and Introduction describe nonlinear mapping into a high-dimensional feature space, and §6.2.1 reports good performance without apparent overfitting as feature-space dimensionality increases. The paper does not address damage identification or its growing popularity, and its digit-recognition experiments do not establish a superior ability to handle small samples. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe generalization error of the SVM is independent of the number of dimensions.
“This makes the generalization error of the SVM independent of the number of dimensions.”
From Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors (2018) · cites this paper as [4]
Traditional methods may require complex feature matching algorithms such as SIFT, SURF, or additional classifiers such as SVM, whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, simplifying the process.
“Traditional methods may require complex feature matching algorithms (e.g., SIFT, SURF) or additional classifiers (e.g., SVM), whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, which simplifies the process [27,28].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [28]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Self-citation(1989) · neural information processing systems · Cited by 3,736 · see citing works
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002 · see citing works
Advances in Neural Information Processing Systems 37
Touretzky, D. S. (2024) · doi:10.52202/079017 · Cited by 2,874 · see citing works
Mullin, A., & Rosenblatt, F. F. (1963) · American Mathematical Monthly · doi:10.2307/2312103 · Cited by 2,223 · see citing works
Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
Айзерман, М. А. (1964) · Automation and Remote Control · Cited by 1,478 · see citing works
Woodhouse, C. (2024) · Routledge eBooks · doi:10.4324/9781003463108-19 · Cited by 1,039 · see citing works
Methods of mathematical physics, Vol. 2, Partial differential equations
Courant, R., & Hilbert, D. (1989) · Wiley eBooks · doi:10.1002/9783527617234 · Cited by 738 · see citing works
Comparison of classifier methods: a case study in handwritten digit recognition
Bottou, L., Cortes, C., Denker, J. S., Drucker, H., Guyon, I., Jackel, L. D., LeCun, Y., Müller, U., Säckinger, E., Simard, P., et al. (2002) · IAPR International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.1994.576879 · Cited by 624 · see citing works
Self-citationClassification into two Multivariate Normal Distributions with Different Covariance Matrices
Anderson, T. W., & Bahadur, R. R. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704568 · Cited by 250 · see citing works
Neural-Network and k-Nearest-neighbor Classifiers
Bromley, J. M., & Säckinger, E. (1991) · Open Research Online (The Open University) · Cited by 31 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [10]10. Parker, D.B. (1985). Learning logic. Technical Report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA.
- [14]14. Vapnik, V.N. (1982). Estimation ofDependences Based on Empirical Data, Addendum 1, New York: Springer- Verlag. Received May 15, 1993 Accepted February 20, 1995 Final Manuscript March 8, 1995
Cited by
Sorted by citations
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Rayyan-a web and mobile app for systematic reviews.
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016) · Systematic Reviews · doi:10.1186/s13643-016-0384-4 · Cited by 26,092 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,525 · see citing works
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954 · see citing works
Self-citationMasked Autoencoders Are Scalable Vision Learners
He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01553 · Cited by 8,489 · see citing works
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Schölkopf, B., Smola, A. J., & Müller, K. (1998) · Neural Computation · doi:10.1162/089976698300017467 · Cited by 8,121 · see citing works
A comparison of methods for multiclass support vector machines
Hsu, C., & Lin, C. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991427 · Cited by 6,861 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306 · see citing works
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,305 · see citing works
Self-citationLarge-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795 · see citing works
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,564 · see citing works
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545 · see citing works
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,070 · see citing works
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791 · see citing works
Chapelle, O., Schölkopf, B., & Zien, A. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,560 · see citing works
Optimizing search engines using clickthrough data
Joachims, T. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775067 · Cited by 3,921 · see citing works
Probabilistic Principal Component Analysis
Tipping, M. E., & Bishop, C. M. (1999) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00196 · Cited by 3,679 · see citing works
An introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,485 · see citing works
Support vector machines in remote sensing: a review
Mountrakis, G., Im, J., & Ogole, C. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.11.001 · Cited by 3,473 · see citing works
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
Random forest classifier for remote sensing classification
Pal, M. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160412331269698 · Cited by 3,293 · see citing works
Prediction, learning, and games
Cesa-Bianchi, N., & Lugosi, G. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546921 · Cited by 4,660 · see citing works
Sentiment analysis algorithms and applications: A survey
Medhat, W., Hassan, A., & Korashy, H. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2014.04.011 · Cited by 3,265 · see citing works
Optimization Methods for Large-Scale Machine Learning
Bottou, L., Curtis, F. E., & Nocedal, J. M. (2018) · SIAM Review · doi:10.1137/16m1080173 · Cited by 3,233 · see citing works
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107 · see citing works
A Short Introduction to Boosting
Freund, Y., & Schapire, R. E. (1999) · Cited by 2,991 · see citing works
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997 · see citing works
(2015) · Cited by 2,857 · see citing works
A Survey on Evaluation of Large Language Models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 2,842 · see citing works
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837 · see citing works
Support Vector Machines for Classification and Regression
Gunn, S. R. (1998) · Cited by 2,835 · see citing works
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799 · see citing works
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas169 · Cited by 2,714 · see citing works
(1998) · Cited by 2,679 · see citing works
A re-examination of text categorization methods
Yang, Y., & Liu, X. (1999) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/312624.312647 · Cited by 2,670 · see citing works
A Survey on Human Activity Recognition using Wearable Sensors
Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662 · see citing works
Modern Applied Statistics with S Fourth edition
Venables, W. N., & Ripley, B. D. (2002) · Cited by 2,538 · see citing works
Training support vector machines: an application to face detection
Osuna, E., Freund, R. M., & Girosi, F. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1997.609310 · Cited by 2,481 · see citing works
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.10.906 · Cited by 2,449 · see citing works
Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,450 · see citing works
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338 · see citing works
Medical hyperspectral imaging: a review
Lu, G., & Fei, B. (2014) · Journal of Biomedical Optics · doi:10.1117/1.jbo.19.1.010901 · Cited by 2,332 · see citing works
Boosting the margin: a new explanation for the effectiveness of voting methods
Bartlett, P. L., Freund, Y., Lee, W. S., & Schapire, R. E. (1998) · The Annals of Statistics · doi:10.1214/aos/1024691352 · Cited by 2,541 · see citing works
Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,287 · see citing works
Choosing Multiple Parameters for Support Vector Machines
Chapelle, O., Vapnik, V. N., Bousquet, O., & Mukherjee, S. (2002) · Machine Learning · doi:10.1023/a:1012450327387 · Cited by 2,215 · see citing works
Self-citationLearning Spatially Regularized Correlation Filters for Visual Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.490 · Cited by 2,171 · see citing works
Tabular data: Deep learning is not all you need
Shwartz-Ziv, R., & Armon, A. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.011 · Cited by 2,172 · see citing works
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1402008 · Cited by 2,156 · see citing works
Stochastic Gradient Descent Tricks
Bottou, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_25 · Cited by 2,097 · see citing works
Implementation of machine-learning classification in remote sensing: an applied review
Maxwell, A. E., Warner, T. A., & Fang, F. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1433343 · Cited by 2,152 · see citing works
The Boosting Approach to Machine Learning: An Overview
Schapire, R. E. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_9 · Cited by 2,060 · see citing works
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
Cai, Z., & Vasconcelos, N. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2956516 · Cited by 1,984 · see citing works
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967 · see citing works
(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959 · see citing works
Kernel principal component analysis
Schölkopf, B., Smola, A. J., & Müller, K. (1997) · Lecture notes in computer science · doi:10.1007/bfb0020217 · Cited by 1,959 · see citing works
Quantum Support Vector Machine for Big Data Classification
Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951 · see citing works
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,903 · see citing works
Advances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,902 · see citing works
On the algorithmic implementation of multiclass kernel-based vector machines
Crammer, K., & Singer, Y. (2002) · Cited by 1,902 · see citing works
A Dataset for Breast Cancer Histopathological Image Classification
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2496264 · Cited by 1,885 · see citing works
CatBoost for big data: an interdisciplinary review
Hancock, J., & Khoshgoftaar, T. M. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00369-8 · Cited by 1,916 · see citing works
Twin Support Vector Machines for Pattern Classification
Jayadeva, Khemchandani, R., & Chandra, S. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1068 · Cited by 1,804 · see citing works
An assessment of support vector machines for land cover classification
Huang, C., Davis, L. S., & Townshend, J. R. G. (2002) · International Journal of Remote Sensing · doi:10.1080/01431160110040323 · Cited by 1,801 · see citing works
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770 · see citing works
Kernel methods in machine learning
Hofmann, T., Schölkopf, B., & Smola, A. J. (2008) · The Annals of Statistics · doi:10.1214/009053607000000677 · Cited by 1,758 · see citing works
A Unifying View of Sparse Approximate Gaussian Process Regression
Quiñonero-Candela, J., & Rasmussen, C. E. (2005) · doi:10.5555/1046920.1194909 · Cited by 1,739 · see citing works
Trends in extreme learning machines: A review
Huang, G. M., Huang, G.-B., Song, S., & You, K. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.10.001 · Cited by 1,737 · see citing works
AutoML: A survey of the state-of-the-art
He, X. J., Zhao, K., & Chu, X. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106622 · Cited by 1,778 · see citing works
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727 · see citing works
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726 · see citing works
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 1,726 · see citing works
Challenges in Representation Learning: A Report on Three Machine Learning Contests
Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-42051-1_16 · Cited by 1,719 · see citing works
Recent advances in techniques for hyperspectral image processing
Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps‐Valls, G., Chanussot, J., Fauvel, M., Gamba, P. E., Gualtieri, A. G., et al. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.07.028 · Cited by 1,705 · see citing works
Support vector machine in machine condition monitoring and fault diagnosis
Widodo, A., & Yang, B.-S. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2006.12.007 · Cited by 1,650 · see citing works
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647 · see citing works
The importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639 · see citing works
Probability Estimates for Multi-class Classification by Pairwise Coupling
Wu, T., Lin, C., & Weng, R. C. (2004) · Journal of Machine Learning Research · doi:10.5555/1005332.1016791 · Cited by 1,673 · see citing works
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
Song, T., Zheng, W., Song, P., & Cui, Z. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2817622 · Cited by 1,610 · see citing works
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.06.004 · Cited by 1,611 · see citing works
López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013) · Information Sciences · doi:10.1016/j.ins.2013.07.007 · Cited by 1,594 · see citing works
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Fuentes, A., Yoon, S., Kim, S. R., & Park, D. J. (2017) · Sensors · doi:10.3390/s17092022 · Cited by 1,651 · see citing works
Harmonization of cortical thickness measurements across scanners and sites
Fortin, J.-P., Cullen, N., Sheline, Y. I., Taylor, W. D., Aselcioglu, I., Cook, P. A., Adams, P., Cooper, C., Fava, M., McGrath, P. J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.024 · Cited by 1,580 · see citing works
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2015.01.001 · Cited by 1,576 · see citing works
Support Vector Machines: Theory and Applications
Wang, L. (2005) · Studies in fuzziness and soft computing · doi:10.1007/b95439 · Cited by 1,550 · see citing works
Lin, C.-F., & Wang, S. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991432 · Cited by 1,497 · see citing works
Fu, J., Zheng, H., & Mei, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.476 · Cited by 1,459 · see citing works
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
Support vector machines for histogram-based image classification
Chapelle, O., Haffner, P., & Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788646 · Cited by 1,468 · see citing works
Self-citationInductive learning algorithms and representations for text categorization
Dumais, S., Platt, J. C., Heckerman, D. E., & Sahami, M. (1998) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/288627.288651 · Cited by 1,463 · see citing works
- Authors
- Cortes, C., & Vapnik, V.
- Affiliations
- AT&T (United States) (US)
- Venue
- Machine Learning
- Published
- 1995-09
- Volume
- 20
- Issue
- 3
- Pages
- 273-297
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W4239510810
- Cited by
- 59,421
- References
- 14
- Citations by year
- 2026: 4864 · 2025: 5184 · 2024: 4570 · 2023: 4472 · 2022: 4105 · 2021: 4479 · 2020: 4258 · 2019: 3920 · 2018: 3567 · 2017: 2895 · 2016: 2583 · 2015: 2464 · 2014: 2282 · 2013: 1923 · 2012: 1459
- FWCI
- 33.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- binary classification, high-dimensional feature space, model generalization, optical character recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
