The DOI matches its Crossref record; 50 of the 55 references it cites were found in a registry or an index.
LIBSVM: A library for support vector machines
Abstract
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
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
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 6,923
Self-citationEstimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056
Self-citationProbabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
Platt, J. C. (1999) · Cited by 4,834
Making Large-Scale SVM Learning PracticalPreprint
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799
Flagged first, then by citations
5 of the 55 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
- 10.1002/asmb.v21:2 · DOI
- 10.1023/A:1022627411411 · DOI
- Joachims T., Advances in Kernel Methods -- Support Vector Learning, B. Schölkopf
- Kressel U. H.-G., Advances in Kernel Methods -- Support Vector Learning, B. Schölkopf
- Lee Y.-J., Proceedings of the 1st SIAM International Conference on Data Mining.
- List N., Proceedings of the 22nd Annual Conference on Computational Learning Theory.
- Osuna E., Proceedings of CVPR'97
- Platt J. C., Advances in Kernel Methods - Support Vector Learning, B. Schölkopf
- Platt J. C., Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf, and D
Cited by
Sorted by citations
Items 201–300 of 1,228
Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering
Lussier, F., Thibault, V., Charron, B., Wallace, G. Q., & Masson, J. (2020) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2019.115796 · Cited by 682
A data-driven approach to cleaning large face datasets
Ng, H., & WINKLER, S. S. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025068 · Cited by 673
Problem formulations and solvers in linear SVM: a review
Chauhan, V. K., Dahiya, K., & Sharma, A. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-018-9614-6 · Cited by 695
Fast and reliable prediction of noncoding RNAs
Stefan Washietl, A., Hofacker, I. L., & Stadler, P. F. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409169102 · Cited by 658
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664
Varoquaux, G. P., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., & Mueller, A. (2015) · GetMobile Mobile Computing and Communications · doi:10.1145/2786984.2786995 · Cited by 663
Li, S., & Deng, W. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2868382 · Cited by 688
CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave
Oosterhof, N. N., Connolly, A. C., & Haxby, J. V. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00027 · Cited by 660
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
Li, J., Haq, A. U., Din, S. U., Khan, J., Khan, A., & Saboor, A. (2020) · IEEE Access · doi:10.1109/access.2020.3001149 · Cited by 687
GraphMAE: Self-Supervised Masked Graph Autoencoders
Hou, Z., Liu, X., Cen, Y., Dong, Y., Yang, H., Wang, C., & Tang, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539321 · Cited by 672
Class segmentation and object localization with superpixel neighborhoods
Fulkerson, B., Vedaldi, A., & Soatto, S. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459175 · Cited by 657
Latent aspect rating analysis on review text data
Wang, H., Lu, Y., & Zhai, C. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835903 · Cited by 651
Human fall detection on embedded platform using depth maps and wireless accelerometer
Kwolek, B., & Kepski, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.09.005 · Cited by 650
RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices
Wang, H., Zhang, D., Wang, Y., Ma, J., Wang, Y., & Li, S. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2557795 · Cited by 648
OpenMS – An open-source software framework for mass spectrometry
Sturm, M., Bertsch, A., Gröpl, C., Hildebrandt, A., Hussong, R., Lange, E., Pfeifer, N., Schulz-Trieglaff, O. B., Zerck, A., Reinert, K., et al. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-163 · Cited by 656
Website Fingerprinting at Internet Scale
Panchenko, A., Lanze, F., Zinnen, A., Henze, M., Pennekamp, J., Wehrle, K., & Engel, T. (2016) · Proceedings 2016 Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23477 · Cited by 645
Machine Learning Methods for Attack Detection in the Smart Grid
Özay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2404803 · Cited by 645
Efficient Graphlet Kernels for Large Graph Comparison
Sherashidze, N., Vishwanathan, S. V. N., Petri, T., Mehlhorn, K., & Borgwardt, K. M. (2009) · MPG.PuRe (Max Planck Society) · Cited by 644
Ahmad, M. W., Reynolds, J. F., & Rezgui, Y. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.08.207 · Cited by 646
Artificial intelligence: a survey on evolution, models, applications and future trends
Lu, Y. (2019) · Journal of Management Analytics · doi:10.1080/23270012.2019.1570365 · Cited by 641
Towards optimal bag-of-features for object categorization and semantic video retrieval
Jiang, Y., Ngo, C., & Yang, J. J. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282352 · Cited by 640
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637
Modulation Classification Based on Signal Constellation Diagrams and Deep Learning
Peng, S., Jiang, H., Wang, H., Alwageed, H. S., Zhou, Y., Sebdani, M. M., & Yao, Y. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2850703 · Cited by 637
Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping
Stelzer, J., Buschmann, T., Lohmann, G., Margulies, D. S., Trampel, R., & Turner, R. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00066 · Cited by 636
Patil, M. A., Tagade, P., Hariharan, K. S., Kolake, S. M., Song, T., Yeo, T., & Doo, S. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.119 · Cited by 631
Supervised prediction of drug–target interactions using bipartite local models
Kevin Bleakley, A., & Yamanishi, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp433 · Cited by 638
Deep learning for estimating building energy consumption
Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2016.02.005 · Cited by 627
Learning to detect phishing emails
Fette, I., Sadeh, N., & Tomasic, A. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242660 · Cited by 635
Sarcasm as Contrast between a Positive Sentiment and Negative Situation
Riloff, E., Qadir, A., Surve, P., Silva, L. D., Gilbert, N., & Huang, R. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1066 · Cited by 625
Generating query substitutions
Jones, R., Rey, B., Madani, O., & Greiner, W. (2006) · International World Wide Web Conference (WWW) · doi:10.1145/1135777.1135835 · Cited by 622
Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018) · Mobile Information Systems · doi:10.1155/2018/3860146 · Cited by 621
The challenge problem for automated detection of 101 semantic concepts in multimedia
Snoek, C. G. M., Worring, M., van Gemert, J. C., Geusebroek, J., & Smeulders, A. W. M. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180727 · Cited by 621
Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring
Kachuee, M., Kiani, M. M., Mohammadzade, H., & Shabany, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2580904 · Cited by 621
Template Matching Techniques in Computer Vision
Brunelli, R. (2009) · Wiley eBooks · doi:10.1002/9780470744055 · Cited by 892
False rumors detection on Sina Weibo by propagation structures
Wu, K., Yang, S. S., & Zhu, K. Q. (2015) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2015.7113322 · Cited by 617
Painful data: The UNBC-McMaster shoulder pain expression archive database
Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771462 · Cited by 634
Fast Kernel Classifiers With Online And Active Learning
Bordes, A., Ertekin, Ş., Weston, J., & Bottou, L. (2005) · Cited by 616
Text mining for market prediction: A systematic review
Nassirtoussi, A. K., Aghabozorgi, S. R., Wah, T. Y., & Ngo, D. C. L. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.06.009 · Cited by 628
Violent flows: Real-time detection of violent crowd behavior
Hassner, T., Itcher, Y., & Kliper-Gross, O. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239348 · Cited by 612
Using Free Energy Principle For Blind Image Quality Assessment
Gu, K., Zhai, G., Yang, X., & Zhang, W. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2373812 · Cited by 606
Chen, Y.-C. (2015) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2014.12.001 · Cited by 611
iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition
Chen, W., Feng, P., Lin, H., & Chou, K. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1450 · Cited by 611
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608
“This type of cascade classification is similar in spirit to our presented second tier deep classifier (ConvNet), but uses state-of-the-art shallow classifiers (libSVM [47] and sparse linear fusion via the Relevance Vector Machine [48]).”
ResultZhong, Y., Hu, X. Y., Luo, C., Wang, X., Zhao, J. J., & Zhang, L. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112012 · Cited by 648
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15027-z · Cited by 607
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607
Adaptation Regularization: A General Framework for Transfer Learning
Long, M., Wang, J., Ding, G., Pan, S. J., & Yu, P. S. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.111 · Cited by 609
Efficient kNN classification algorithm for big data
Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.08.112 · Cited by 603
Effective data generation for imbalanced learning using conditional generative adversarial networks
Douzas, G., & Bação, F. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.030 · Cited by 633
Yao, X., Tham, L. G., & Dai, F. C. (2008) · Geomorphology · doi:10.1016/j.geomorph.2008.02.011 · Cited by 602
Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.02.057 · Cited by 602
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Tu, Z., & Bai, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.186 · Cited by 578
Borderline over-sampling for imbalanced data classification
Nguyen, H. M., Cooper, E. W., & Kamei, K. (2011) · International Journal of Knowledge Engineering and Soft Data Paradigms · doi:10.1504/ijkesdp.2011.039875 · Cited by 629
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
Kim, G., Lee, S. M., & Kim, S. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.08.066 · Cited by 593
Karatzoglou, A., MEYER, D. A., & Hornik, K. (2006) · Journal of Statistical Software · doi:10.18637/jss.v015.i09 · Cited by 592
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease
Shamonin, D. P. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00050 · Cited by 582
Sitaram, R., Zhang, H., Guan, C., Thulasidas, M., Hoshi, Y., Ishikawa, A., Shimizu, K., & Birbaumer, N. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.005 · Cited by 598
Kernel Codebooks for Scene Categorization
van Gemert, J. C., Geusebroek, J., Veenman, C. J., & Smeulders, A. W. M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_52 · Cited by 606
Recent Advances in Electrochemical Biosensors: Applications, Challenges, and Future Scope
Singh, A., Sharma, A., Ahmed, A., Sundramoorthy, A. K., Furukawa, H., Arya, S. K., & Khosla, A. (2021) · Biosensors · doi:10.3390/bios11090336 · Cited by 793
Multi-class active learning for image classification
Joshi, A. J., Porikli, F., & Papanikolopoulos, N. P. (2009) · 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.2009.5206627 · Cited by 588
Zhang, X., Liang, Y., Zhou, J., & Zang, Y. (2015) · Measurement · doi:10.1016/j.measurement.2015.03.017 · Cited by 586
Question classification using support vector machines
Zhang, D., & Lee, W. S. (2003) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/860435.860443 · Cited by 585
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts
Zhao, Z., Resnick, P., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741637 · Cited by 583
Bui, D. T., Pradhan, B., Löfman, O., & Revhaug, I. (2012) · Mathematical Problems in Engineering · doi:10.1155/2012/974638 · Cited by 597
Busto, P. P., & Gall, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.88 · Cited by 568
Mathematics for Machine Learning
Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108679930 · Cited by 614
Gait recognition using wifi signals
Wang, W., Liu, A. X., & Shahzad, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971670 · Cited by 582
A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods
Lin, H.-T. (2005) · Cited by 587
Discriminating Gender on Twitter
Burger, J. D., Henderson, J. C., Kim, G., & Zarrella, G. (2011) · Cited by 586
Hyperspectral Image Classification With Deep Feature Fusion Network
Song, W., Shutao Li, A., Fang, L., & Lu, T. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2794326 · Cited by 586
Automatic classification of defective photovoltaic module cells in electroluminescence images
Deitsch, S., Christlein, V., Berger, S., Buerhop‐Lutz, C., Maier, A., Gallwitz, F., & Rieß, C. (2019) · Solar Energy · doi:10.1016/j.solener.2019.02.067 · Cited by 607
The Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities
Kuehne, H., Arslan, A., & Serre, T. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.105 · Cited by 571
Reliability of dissimilarity measures for multi-voxel pattern analysis
Walther, A., Nili, H., Ejaz, N., Alink, A., Kriegeskorte, N., & Diedrichsen, J. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.012 · Cited by 567
MitoFates: Improved Prediction of Mitochondrial Targeting Sequences and Their Cleavage Sites*
Fukasawa, Y., Tsuji, J., Fu, S.-C., Tomii, K., Horton, P., & Imai, K. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.043083 · Cited by 573
Comparative study on classifying human activities with miniature inertial and magnetic sensors
Altun, K., Barshan, B., & Tunçel, O. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.04.019 · Cited by 564
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
Lan, M., Tan, C. L., Su, J., & Lu, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.110 · Cited by 564
Yildiz, B., Bilbao, J. I., & Sproul, A. B. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.02.023 · Cited by 563
Computational Color Constancy: Survey and Experiments
Gijsenij, A., Gevers, T., & van de Weijer, J. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2118224 · Cited by 562
Segmentation and classification of hyperspectral images using watershed transformation
Tarabalka, Y., Chanussot, J., & Benediktsson, J. A. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.01.016 · Cited by 562
Early diagnosis of Alzheimer's disease with deep learning
Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. D. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868045 · Cited by 559
Probability Models for Open Set Recognition
Scheirer, W. J., Jain, L. P., & Boult, T. E. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321392 · Cited by 630
Universal Approximation Capability of Broad Learning System and Its Structural Variations
Chen, C. L. P., Liu, Z., & Feng, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2866622 · Cited by 559
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Xu, D., Ricci, E., Yan, Y., Song, J., & Sebe, N. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.8 · Cited by 553
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
Jindal, A., Dua, A., Kaur, K., Singh, M. K., Kumar, N., & Mishra, S. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2543145 · Cited by 551
Domain Transfer Multiple Kernel Learning
Duan, L., Tsang, I. W.-H., & Xu, D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.114 · Cited by 543
Liu, Z., Zhang, X.-Y., Shi, Y., Wang, L., Zhu, H., Tang, Z., Wang, S., Li, X.-T., Tian, J., & Sun, Y. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-1038 · Cited by 548
A deep learning approach to detection of splicing and copy-move forgeries in images
Rao, Y., & Ni, J. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823911 · Cited by 554
Saliency-Guided Unsupervised Feature Learning for Scene Classification
Zhang, F., Du, B., & Zhang, L. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2357078 · Cited by 550
BagBoosting for tumor classification with gene expression data
Dettling, M. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth447 · Cited by 544
Limited options for low-global-warming-potential refrigerants
McLinden, M. O., Brown, J. S., Brignoli, R., Kazakov, A. F., & Domanski, P. A. (2017) · Nature Communications · doi:10.1038/ncomms14476 · Cited by 542
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark
Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130508 · Cited by 541
Evolving support vector machines using fruit fly optimization for medical data classification
Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B., & Liu, D. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.01.002 · Cited by 539
Why is Real-World Visual Object Recognition Hard?
Pinto, N., Cox, D., & DiCarlo, J. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0040027 · Cited by 560
Financial time series forecasting using independent component analysis and support vector regression
Lu, C.-J., Lee, T., & Chiu, C. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.02.001 · Cited by 538
Lift: Multi-Label Learning with Label-Specific Features
Zhang, M.-L., & Wu, L. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2339815 · Cited by 538
High-throughput prediction of protein antigenicity using protein microarray data
Magnan, C. N., Zeller, M. D., Kayala, M. A., Vigil, A., Randall, A. Z., Felgner, P. L., & Baldi, P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq551 · Cited by 537
Clustering-Based Hyperspectral Band Selection Using Information Measures
Martínez-Usó, A., Pla, F., Sotoca, J. M., & Garcı́a-Sevilla, P. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.904951 · Cited by 534
Discriminative Embeddings of Latent Variable Models for Structured DataPreprint
Dai, H., Dai, B., & Song, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05629 · Cited by 534
A Tutorial on Multilabel Learning
Gibaja, E., & Ventura, S. (2015) · ACM Computing Surveys · doi:10.1145/2716262 · Cited by 530
Metadata
- Authors
- Chang, C.-C., & Lin, C.-J.
- Affiliations
- National Taiwan University (TW)
- ORCID
- Lin, Chih-Jen
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2011-04
- Volume
- 2
- Issue
- 3
- Pages
- 1-27
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W2153635508
- Cited by
- 40,918
- References
- 55
- Citations by year
- 2026: 462 · 2025: 825 · 2024: 916 · 2023: 1155 · 2022: 1487 · 2021: 1910 · 2020: 2233 · 2019: 2526 · 2018: 2850 · 2017: 3142 · 2016: 3468 · 2015: 3562 · 2014: 3302 · 2013: 2936 · 2012: 2306
- FWCI
- 3060.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Funders
- National Science Council Taiwan (NSC 89-2213-E-002-013NSC 89-2213-E-002-106)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- LIBSVM, support vector machine, convergence analysis, multi-class classification, probability estimates, parameter selection, software implementation
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