Editing Support vector machine (section)

== Applications ==
SVMs can be used to solve various real-world problems:
* SVMs are helpful in [[Text categorization|text and hypertext categorization]], as their application can significantly reduce the need for labeled training instances in both the standard inductive and [[Transduction (machine learning)|transductive]] settings.<ref>{{cite book |last1=Joachims |first1=Thorsten |title=Machine Learning: ECML-98 |chapter=Text categorization with Support Vector Machines: Learning with many relevant features |volume=1398 |date=1998 |pages=137–142 |doi=10.1007/BFb0026683 |isbn=978-3-540-64417-0 |publisher=Springer |language=en|series=Lecture Notes in Computer Science |doi-access=free }}</ref> Some methods for [[shallow semantic parsing]] are based on support vector machines.<ref>{{Cite conference |last=Pradhan |first=Sameer S. |display-authors=et al. |date=2 May 2004 |title=Shallow Semantic Parsing using Support Vector Machines |url=http://www.aclweb.org/anthology/N04-1030 |conference=Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004 |publisher=Association for Computational Linguistics |pages=233–240}}</ref>
* [[Image classification|Classification of images]] can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback. This is also true for [[image segmentation]] systems, including those using a modified version SVM that uses the privileged approach as suggested by Vapnik.<ref>Vapnik, Vladimir N.: Invited Speaker. IPMU Information Processing and Management 2014).</ref><ref>{{cite book | chapter-url=https://pdfs.semanticscholar.org/917f/15d33d32062bffeb6401eee9fe71d16d6a84.pdf | s2cid=4154772 | doi=10.1007/978-3-319-16829-6_12 | chapter=Spatial-Taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation | title=Granular Computing and Decision-Making | series=Studies in Big Data | year=2015 | last1=Barghout | first1=Lauren | volume=10 | pages=285–318 | isbn=978-3-319-16828-9 | access-date=2018-01-08 | archive-date=2018-01-08 | archive-url=https://web.archive.org/web/20180108120422/https://pdfs.semanticscholar.org/917f/15d33d32062bffeb6401eee9fe71d16d6a84.pdf | url-status=dead }}</ref>
* Classification of satellite data like [[Synthetic-aperture radar|SAR]] data using supervised SVM.<ref>{{cite arXiv |author=A. Maity |title=Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features|year=2016|eprint=1608.00501|class=cs.CV}}</ref>
* Hand-written characters can be [[Handwriting recognition|recognized]] using SVM.<ref>{{Cite journal |last=DeCoste |first=Dennis |date=2002 |title=Training Invariant Support Vector Machines |url=https://people.eecs.berkeley.edu/~malik/cs294/decoste-scholkopf.pdf |journal=Machine Learning |volume=46 |pages=161–190 |doi=10.1023/A:1012454411458 |s2cid=85843 |doi-access=free }}</ref><ref>{{Cite book|last1=Maitra|first1=D. S.|last2=Bhattacharya|first2=U.|last3=Parui|first3=S. K.|title=2015 13th International Conference on Document Analysis and Recognition (ICDAR) |chapter=CNN based common approach to handwritten character recognition of multiple scripts |date=August 2015|chapter-url=https://ieeexplore.ieee.org/document/7333916|pages=1021–1025|doi=10.1109/ICDAR.2015.7333916|isbn=978-1-4799-1805-8|s2cid=25739012}}</ref>
* The SVM algorithm has been widely applied in the biological and other sciences.  They have been used to classify proteins with up to 90% of the compounds classified correctly. [[Permutation test]]s based on SVM weights have been suggested as a mechanism for interpretation of SVM models.<ref>{{cite journal | pmc=3767485 | year=2013 | last1=Gaonkar | first1=B. | last2=Davatzikos | first2=C. | title=Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification | journal=NeuroImage | volume=78 | pages=270–283 | doi=10.1016/j.neuroimage.2013.03.066 | pmid=23583748 }}</ref><ref>{{Cite journal |url=http://www.aramislab.fr/perso/colliot/files/media2011_remi_published.pdf |url-status=dead |doi=10.1016/j.media.2011.05.007 |title=Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome |year=2011 |last1=Cuingnet |first1=Rémi |last2=Rosso |first2=Charlotte |last3=Chupin |first3=Marie |last4=Lehéricy |first4=Stéphane |last5=Dormont |first5=Didier |last6=Benali |first6=Habib |last7=Samson |first7=Yves |last8=Colliot |first8=Olivier |journal=Medical Image Analysis |volume=15 |issue=5 |pages=729–737 |pmid=21752695 |access-date=2018-01-08 |archive-date=2018-12-22 |archive-url=https://web.archive.org/web/20181222172844/http://www.aramislab.fr/perso/colliot/files/media2011_remi_published.pdf }}</ref> Support vector machine weights have also been used to interpret SVM models in the past.<ref>Statnikov, Alexander; Hardin, Douglas; & Aliferis, Constantin; (2006); [http://www.ccdlab.org/paper-pdfs/NIPS_2006.pdf "Using SVM weight-based methods to identify causally relevant and non-causally relevant variables"], ''Sign'', 1, 4.</ref> Posthoc interpretation of support vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences.