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Gene Selection for Cancer Classification using Support Vector Machines
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Self-citationCortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Self-citationWu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Self-citationCluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
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
Self-citationGolub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
Research works printed but not linked: 2
- Ghigna, C., Moroni, M., Porta, C., Riva, I., & Biamonti, G. (1998). Altered expression of heterogeneous nuclear ribonucleoproteins and SR factors in human. Cancer Research, 58, 5818-5824.
- LeCun, Y., Denker, J. S., & Solla, S. A. (1990). Optimum brain damage. In D. Touretzky (Ed.). Advances in neural information processing systems 2 (pp. 598-605). San Mateo, CA: Morgan Kaufmann.
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Whalen, S., Truty, R. M., & Pollard, K. S. (2016) · Nature Genetics · doi:10.1038/ng.3539 · Cited by 456
Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
Stephansen, J. B., Olesen, A. N., Olsen, M., Ambati, A., Leary, E. B., Moore, H. E., Carrillo, O., Lin, L., Han, F., Yan, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07229-3 · Cited by 455
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
Zhu, Z., Ong, Y.-S., & Dash, M. (2007) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2006.883267 · Cited by 446
Enhanced recursive feature elimination
Chen, X.-W., & Jeong, J. C. (2007) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2007.35 · Cited by 445
Zhou, X., Wen, H., Zhang, Y., Xu, J., & Zhang, W. (2021) · Geoscience Frontiers · doi:10.1016/j.gsf.2021.101211 · Cited by 442
Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease
Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., & Lin, C. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3344998 · Cited by 440
Short-term stock market price trend prediction using a comprehensive deep learning system
Shen, J., & Shafiq, M. O. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00333-6 · Cited by 434
Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images
Bazi, Y., & Melgani, F. (2006) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2006.880628 · Cited by 433
Effective injury forecasting in soccer with GPS training data and machine learning
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., & Medina, D. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201264 · Cited by 419
Haury, A.-C., Gestraud, P., & Vert, J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028210 · Cited by 418
Samiee, K., Kovács, P., & Gabbouj, M. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2360101 · Cited by 417
Song, H., Yoo, Y., Hwang, J., Na, Y.-C., & Kim, H. S. (2016) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2015.08.021 · Cited by 416
Zhou, J., Li, X., & Shi, X. (2011) · Safety Science · doi:10.1016/j.ssci.2011.08.065 · Cited by 413
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Shevade, S., & Keerthi, S. S. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg308 · Cited by 411
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Bosl, W. J., Tager-Flusberg, H. B., & Nelson, C. A. (2018) · Scientific Reports · doi:10.1038/s41598-018-24318-x · Cited by 410
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Bi, J., Bennett, K. P., Embrechts, M. J., Breneman, C. M., Song, M., Guyon, I., & Elisseeff, A. (2003) · Cited by 409
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Kuo, B., Ho, H.-H., Li, C., Hung, C., & Taur, J. S. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2262926 · Cited by 408
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Tan, A. C., Naiman, D. Q., Xu, L., Winslow, R. L., & Geman, D. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti631 · Cited by 407
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Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.01.012 · Cited by 405
Supervised feature selection via dependence estimation
Song, L., Smola, A. J., Gretton, A., Borgwardt, K. M., & Bedő, J. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273600 · Cited by 401
Combating the Small Sample Class Imbalance Problem Using Feature Selection
Wasikowski, M., & Chen, X.-W. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.187 · Cited by 401
Recent advances in feature selection and its applications
Li, Y., Li, T., & Liu, H. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1059-8 · Cited by 400
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Amrane, M., Oukid, S., Gagaoua, I. C., & Ensarı, T. (2018) · Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) · doi:10.1109/ebbt.2018.8391453 · Cited by 399
Applications of Support Vector Machines in Chemistry
Ivanciuc, O. (2007) · Reviews in computational chemistry · doi:10.1002/9780470116449.ch6 · Cited by 398
An unsupervised feature selection algorithm based on ant colony optimization
Tabakhi, S., Moradi, P., & Akhlaghian, F. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.03.007 · Cited by 395
Ecker, C., Rocha-Rego, V., Johnston, P., Mourão-Miranda, J., Marquand, A. F., Daly, E., Brammer, M. J., Murphy, C. M., & Murphy, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.024 · Cited by 393
Fehr, D., Veeraraghavan, H., Wibmer, A. G., Gondo, T., Matsumoto, K., Vargas, H. A., Sala, E., Hricak, H., & Deasy, J. O. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1505935112 · Cited by 391
Discriminative Semi-Supervised Feature Selection Via Manifold Regularization
Xu, Z., King, I., Lyu, M. R.-T., & Jin, R. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2047114 · Cited by 390
Exploring EEG Features in Cross-Subject Emotion Recognition
Li, X., Song, D., Zhang, P., Zhang, Y., Hou, Y., & Hu, B. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00162 · Cited by 390
Comparing support vector machines to PLS for spectral regression applications
Thissen, U., Pepers, M., Üstün, B., Melssen, W. J., & Buydens, L. M. C. (2004) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.01.002 · Cited by 388
Random forests for global sensitivity analysis: A selective review
Antoniadis, A., Lambert‐Lacroix, S., & Poggi, J. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107312 · Cited by 388
SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier
Huang, M. L., Hung, Y., Lee, W. M., Li, R., & Jiang, B.-R. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/795624 · Cited by 386
Pacifici, F., Chini, M., & Emery, W. J. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.02.014 · Cited by 385
Liú, D., Sun, D., & Zeng, X. (2013) · Food and Bioprocess Technology · doi:10.1007/s11947-013-1193-6 · Cited by 383
Machine learning and integrative analysis of biomedical big data
Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P. (2019) · Genes · doi:10.3390/genes10020087 · Cited by 380
Moradi, P., & Gholampour, M. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.01.044 · Cited by 380
Fault detection and diagnosis in process data using one-class support vector machines
Mahadevan, S., & Shah, S. L. (2009) · Journal of Process Control · doi:10.1016/j.jprocont.2009.07.011 · Cited by 377
Early diagnosis of Parkinson’s disease using machine learning algorithms
Şentürk, Z. K. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109603 · Cited by 377
A feature learning approach based on XGBoost for driving assessment and risk prediction
Shi, X., Wong, Y. D., Li, M. Z. F., Palanisamy, C., & Chai, C. (2019) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.05.005 · Cited by 376
Analysis of Metabolomic Data Using Support Vector Machines
Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M. (2008) · Analytical Chemistry · doi:10.1021/ac800954c · Cited by 374
Improved salp swarm algorithm based on particle swarm optimization for feature selection
Ibrahim, R. A., Ewees, A. A., Oliva, D. A., Elaziz, M. A., & Lu, S. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1031-9 · Cited by 374
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements
Yong Fan, A., Shen, D. G., Gur, R. C., Gur, R. E., & Davatzikos, C. A. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.886812 · Cited by 370
Can SVM be used for automatic EEG detection of drowsiness during car driving?
Yeo, M. V. M., Li, X., Shen, K., & Wilder-Smith, E. P. (2008) · Safety Science · doi:10.1016/j.ssci.2008.01.007 · Cited by 369
Classification of gene microarrays by penalized logistic regression
Zhu, J. (2004) · Biostatistics · doi:10.1093/biostatistics/5.3.427 · Cited by 369
Riaz, F., Hassan, A., Rehman, S. U., Niazi, I. K., & Dremstrup, K. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2441835 · Cited by 364
Highly Comparative Feature-Based Time-Series Classification
Fulcher, B., & Jones, N. S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2316504 · Cited by 363
High dimensional data classification and feature selection using support vector machines
Ghaddar, B., & Naoum‐Sawaya, J. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.08.040 · Cited by 360
The versatile use of exhaled volatile organic compounds in human health and disease
Boots, A. W., van Berkel, J. J. B. N., Dallinga, J. W., Smolinska, A., Wouters, E. F., & van Schooten, F. (2012) · Journal of Breath Research · doi:10.1088/1752-7155/6/2/027108 · Cited by 359
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Huang, J., Cai, Y., & Xu, X. (2007) · Pattern Recognition Letters · doi:10.1016/j.patrec.2007.05.011 · Cited by 358
Support vector machines for credit scoring and discovery of significant features
Bellotti, A. G., & Crook, J. N. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.005 · Cited by 357
Review of soft sensor methods for regression applications
Souza, F. A. A., Araújo, R. A. M., & Mendes, J. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.12.011 · Cited by 357
Hybrid feature selection by combining filters and wrappers
Hsu, H.-H., Hsieh, C.-W., & Lu, M.-D. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.12.156 · Cited by 356
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
Sun, Y., Todorović, S., & Goodison, S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.190 · Cited by 351
Multivariate classification of social anxiety disorder using whole brain functional connectivity
Liu, F., Guo, W., Fouché, J., Wang, Y., Wang, W., Ding, J., Zeng, L., Qiu, C., Gong, Q., Zhang, W., et al. (2013) · Brain Structure and Function · doi:10.1007/s00429-013-0641-4 · Cited by 349
Tumor origin detection with tissue-specific miRNA and DNA methylation markers
Tang, W., Wan, S., Yang, Z., Teschendorff, A. E., & Zou, Q. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx622 · Cited by 348
Zhang, B., Shi, H., & Wang, H. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s410301 · Cited by 347
Feature selection for support vector machines by means of genetic algorithm
Fröhlich, H., Chapelle, O., & Schölkopf, B. (2004) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/tai.2003.1250182 · Cited by 347
Feature selection via dependence maximization
Song, L., Smola, A. J., Gretton, A., Bedő, J., & Borgwardt, K. M. (2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2343691 · Cited by 347
Review of Classification Methods on Unbalanced Data Sets
Wang, L., Han, M., Li, X., Zhang, N., & Cheng, H. (2021) · IEEE Access · doi:10.1109/access.2021.3074243 · Cited by 346
Li, Q., & Lin, N. (2010) · Bayesian Analysis · doi:10.1214/10-ba506 · Cited by 345
Learning with Support Vector Machines
Campbell, C., & Ying, Y. (2011) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00324ed1v01y201102aim010 · Cited by 343
Predicting property prices with machine learning algorithms
Ho, W. K. O., Tang, B., & Wong, S. W. (2020) · Journal of Property Research · doi:10.1080/09599916.2020.1832558 · Cited by 343
Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets
Best, M. G., Sol, N., Veld, S. G. J. G. I. ‘., Vancura, A., Muller, M., Niemeijer, A.-L. N., Fejes, A. V., Tjon‐Kon‐Fat, L., Veld, A. E. H. I. ‘., Leurs, C. E., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.07.004 · Cited by 341
EEG-based neonatal seizure detection with Support Vector Machines
Temko, A., Thomas, E., Marnane, W., Lightbody, G., & Boylan, G. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.06.034 · Cited by 339
Lin, S.-W., Lee, Z.-J., Chen, S., & Tseng, T.-Y. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.10.012 · Cited by 339
Feature selection methods for big data bioinformatics: A survey from the search perspective
Wang, L., Wang, Y., & Chang, Q. (2016) · Methods · doi:10.1016/j.ymeth.2016.08.014 · Cited by 339
Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection
Zhu, X., Li, X., Zhang, S., Ju, C., & Wu, X. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2521602 · Cited by 338
A tutorial on support vector machine-based methods for classification problems in chemometrics
Luts, J., Ojeda, F., de Plas, R. V., Moor, B. D., Huffel, S. V., & Suykens, J. A. K. (2010) · Analytica Chimica Acta · doi:10.1016/j.aca.2010.03.030 · Cited by 336
Recent advances and emerging challenges of feature selection in the context of big data
Bolón‐Canedo, V., Sánchez‐Maroño, N., & Alonso‐Betanzos, A. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.05.014 · Cited by 335
SVM-RFE With MRMR Filter for Gene Selection
Mundra, P. A., & Rajapakse, J. C. (2009) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2009.2035284 · Cited by 334
Image-based multivariate profiling of drug responses from single cells
Loo, L., Wu, L. F., & Altschuler, S. J. (2007) · Nature Methods · doi:10.1038/nmeth1032 · Cited by 333
Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface
Quadrianto, N., Guan, C., Dat, T. H., & Xue, P. (2007) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2007.369647 · Cited by 331
Somorjai, R. L., Dolenko, B., & Baumgartner, R. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg182 · Cited by 329
Iranzad, R., & Liu, X. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00509-w · Cited by 329
Human pluripotent stem cell-derived neural constructs for predicting neural toxicity
Schwartz, M. P., Hou, Z., Propson, N. E., Zhang, J., Engstrom, C. J., Costa, V. S., Jiang, P., Nguyen, B. K., Bolin, J. M., Daly, W., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516645112 · Cited by 325
Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines
Maldonado, S., Weber, R. W., & Famili, F. A. (2014) · Information Sciences · doi:10.1016/j.ins.2014.07.015 · Cited by 325
A community computational challenge to predict the activity of pairs of compounds
Community, N.-D., Bansal, M., Yang, J., Karan, C., Menden, M. P., Costello, J. C., Tang, H., Xiao, G., Li, Y., Allen, J., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3052 · Cited by 321
Digital image splicing detection based on Markov features in DCT and DWT domain
He, Z., Lu, W., Sun, W., & Huang, J. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.05.014 · Cited by 320
Clinical features of COVID-19 mortality: development and validation of a clinical prediction model
Yadaw, A. S., Li, Y. C., Bose, S., Iyengar, R., Bunyavanich, S., & Pandey, G. S. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30217-x · Cited by 320
Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319
Senoner, J., Netland, T., & Feuerriegel, S. (2022) · Management Science · doi:10.1287/mnsc.2021.4190 · Cited by 317
Ensemble feature selection: Homogeneous and heterogeneous approaches
Seijo-Pardo, B., Porto-Díaz, I., Bolón‐Canedo, V., & Alonso‐Betanzos, A. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.11.017 · Cited by 317
Stable feature selection for biomarker discovery
He, Z., & Yu, W. (2010) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2010.07.002 · Cited by 316
Schulz, M.-A., Yeo, B. T. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., Richards, B., & Bzdok, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18037-z · Cited by 315
HITON: a novel Markov Blanket algorithm for optimal variable selection.
Aliferis, C., Tsamardinos, I., & Statnikov, A. (2003) · Europe PMC (PubMed Central) · Cited by 315
Feature selection for high-dimensional data
Bolón‐Canedo, V., Sánchez‐Maroño, N., & Alonso‐Betanzos, A. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-015-0080-y · Cited by 315
Feature selection with multi-view data: A survey
Zhang, R., Nie, F., Li, X., & Wei, X. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.019 · Cited by 313
Zhang, H., Fu, H., He, X., Wang, C., Jiang, L., Chen, L., & Xie, J. (2020) · Acta Materialia · doi:10.1016/j.actamat.2020.09.068 · Cited by 313
Predicting Personality Using Novel Mobile Phone-Based Metrics
de Montjoye, Y.-A., Quoidbach, J., Robic, F., & Pentland, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37210-0_6 · Cited by 310
Gene selection with guided regularized random forest
Deng, H., & Runger, G. C. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.05.018 · Cited by 309
Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities
Chen, G., Weng, Q., Hay, G. J., & He, Y. (2018) · GIScience & Remote Sensing · doi:10.1080/15481603.2018.1426092 · Cited by 309
Deep support vector machine for hyperspectral image classification
Okwuashi, O., & Ndehedehe, C. E. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107298 · Cited by 304
Roffo, G., Melzi, S., & Cristani, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.478 · Cited by 302
Support vector machines in engineering: an overview
Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., & Camps‐Valls, G. (2014) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1125 · Cited by 301
Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data
Zhang, X., Lu, X. L., Shi, Q., Xu, X., Leung, H. E., Harris, L. N., Iglehart, J. D., Miron, A., Liu, J. S., & Wong, W. H. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-197 · Cited by 300
Unraveling the distributed neural code of facial identity through spatiotemporal pattern analysis
Nestor, A., Plaut, D. C., & Behrmann, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102433108 · Cited by 298
Metadata
- Authors
- Guyon, I., Weston, J., Barnhill, S., & Vapnik, V.
- Affiliations
- Georgia Institute of Technology (US)
- AT&T (United States) (US)
- Corresponding author
- Guyon, Isabelle
- Vapnik, Vladimir
- ORCID
- Guyon, Isabelle
- Venue
- Machine Learning
- Published
- 2002-01
- Volume
- 46
- Issue
- 1-3
- Pages
- 389-422
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2143426320
- Cited by
- 9,954
- References
- 48
- Citations by year
- 2026: 553 · 2025: 676 · 2024: 564 · 2023: 586 · 2022: 576 · 2021: 695 · 2020: 607 · 2019: 571 · 2018: 488 · 2017: 478 · 2016: 408 · 2015: 444 · 2014: 400 · 2013: 400 · 2012: 359
- FWCI
- 38.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Delft Research Centres
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- gene selection, cancer classification, DNA microarrays, gene expression data, recursive feature elimination, support vector machine, leukemia, colon cancer, gene redundancy, genetic diagnosis, drug discovery
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 ›
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