Skip to main content
Erschienen in: Annals of Data Science 2/2014

01.06.2014

Review on: Twin Support Vector Machines

verfasst von: Yingjie Tian, Zhiquan Qi

Erschienen in: Annals of Data Science | Ausgabe 2/2014

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance from the other. TWSVM has lower computational complexity and better generalization ability, therefore in the last few years it has been studied extensively and developed rapidly. Considering the many variants of TWSVM, a systematic survey is needed and helpful to understand and use this family of data mining techniques more easily. The purpose of this paper is to closely review TWSVMs and provide an insightful understanding of current developments, at the same time point out their limitations and highlight the major opportunities and challenges, as well as potential important research directions.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3):273–297 Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3):273–297
2.
Zurück zum Zitat Vapnik VN (1996) The nature of statistical learning theory. Springer, New York Vapnik VN (1996) The nature of statistical learning theory. Springer, New York
3.
Zurück zum Zitat Vapnik VN (1998) Statistical learning theory. Publishing House of Electronics Industry, New York Vapnik VN (1998) Statistical learning theory. Publishing House of Electronics Industry, New York
4.
Zurück zum Zitat Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRef Cristianini N, Taylor JS (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRef
5.
Zurück zum Zitat Fung GM, Mangasarian OL (2001) Multicategory proximal support vector machine classifiers. Mach Learn 59(1—-2):77–97 Fung GM, Mangasarian OL (2001) Multicategory proximal support vector machine classifiers. Mach Learn 59(1—-2):77–97
6.
Zurück zum Zitat Deng NY, Tian YJ (2009) Support vector machines: theory, algorithms and extensions. Science Press, Beijing Deng NY, Tian YJ (2009) Support vector machines: theory, algorithms and extensions. Science Press, Beijing
7.
Zurück zum Zitat Deng NY, Tian YJ, Zhang CH (2012) Support vector machines: optimization based theory, algorithms and extensions. CRC Press, Chapman and Hall, Boca Raton Deng NY, Tian YJ, Zhang CH (2012) Support vector machines: optimization based theory, algorithms and extensions. CRC Press, Chapman and Hall, Boca Raton
8.
Zurück zum Zitat Joachims T (1999) Text categorization with support vector machines: learning with many relevant features. In: Proceedings of 10th European conference on machine learning, pp 137–142 Joachims T (1999) Text categorization with support vector machines: learning with many relevant features. In: Proceedings of 10th European conference on machine learning, pp 137–142
9.
Zurück zum Zitat Lodhi H, Cristianini N, Shawe-Taylor J, Watkins C (2000) Text classification using string kernels. Adv Neural Inf Process Syst 13:563–569 Lodhi H, Cristianini N, Shawe-Taylor J, Watkins C (2000) Text classification using string kernels. Adv Neural Inf Process Syst 13:563–569
10.
Zurück zum Zitat Jonsson K, Kittler J, Matas YP (2002) Support vector machines for face authentication. J Image Vis Comput 20(5):369–375CrossRef Jonsson K, Kittler J, Matas YP (2002) Support vector machines for face authentication. J Image Vis Comput 20(5):369–375CrossRef
11.
Zurück zum Zitat Tefas A, Kotropoulos C, Pitas I (2001) Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans Pattern Anal Mach Intell 23(7):735–746CrossRef Tefas A, Kotropoulos C, Pitas I (2001) Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication. IEEE Trans Pattern Anal Mach Intell 23(7):735–746CrossRef
12.
Zurück zum Zitat Ganapathiraju A, Hamaker J, Picone J (2004) Applications of support vector machines to speech recognition. IEEE Trans Signal Process 52(8):2348–2355CrossRef Ganapathiraju A, Hamaker J, Picone J (2004) Applications of support vector machines to speech recognition. IEEE Trans Signal Process 52(8):2348–2355CrossRef
13.
Zurück zum Zitat Gutta S, Huang JRJ, Jonathon P, Wechsler H (2000) Mixture of experts for classification of gender, ethnic origin, and pose of human. IEEE Trans Neural Netw 11(4):948–960CrossRef Gutta S, Huang JRJ, Jonathon P, Wechsler H (2000) Mixture of experts for classification of gender, ethnic origin, and pose of human. IEEE Trans Neural Netw 11(4):948–960CrossRef
14.
Zurück zum Zitat Shin KS, Lee TS, Kim HJ (2005) An application of support vector machines in bankruptcy prediction model. Expert Syst Appl 28(1):127–135CrossRef Shin KS, Lee TS, Kim HJ (2005) An application of support vector machines in bankruptcy prediction model. Expert Syst Appl 28(1):127–135CrossRef
15.
Zurück zum Zitat Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790CrossRef Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790CrossRef
16.
Zurück zum Zitat Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1):307–319CrossRef Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1):307–319CrossRef
17.
Zurück zum Zitat Liu Y, Zhang D, Lu GG, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282CrossRef Liu Y, Zhang D, Lu GG, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282CrossRef
18.
Zurück zum Zitat Adankon MM, Cheriet M (2009) Model selection for the LS-SVM application to handwriting recognition. Pattern Recogn 42(12):3264–3270CrossRef Adankon MM, Cheriet M (2009) Model selection for the LS-SVM application to handwriting recognition. Pattern Recogn 42(12):3264–3270CrossRef
19.
Zurück zum Zitat Borgwardt KM (2011) Kernel methods in bioinformatics. Handbook of statistical bioinformatics, Part 3. pp 317–334 Borgwardt KM (2011) Kernel methods in bioinformatics. Handbook of statistical bioinformatics, Part 3. pp 317–334
20.
Zurück zum Zitat Khan NM, Ksantini R, Ahmad IS, Boufama B (2012) A novel SVM plus NDA model for classification with an application to face recognition. Pattern Recogn 45(1):66–79CrossRef Khan NM, Ksantini R, Ahmad IS, Boufama B (2012) A novel SVM plus NDA model for classification with an application to face recognition. Pattern Recogn 45(1):66–79CrossRef
21.
Zurück zum Zitat Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74CrossRef Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74CrossRef
22.
Zurück zum Zitat Jayadeva RK, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910CrossRef Jayadeva RK, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910CrossRef
24.
Zurück zum Zitat Ding SF, Yu JZ, Qi BJ, Huang HJ (2014) An overview on twin support vector machines. Artif Intell Rev 42(2):245–252CrossRef Ding SF, Yu JZ, Qi BJ, Huang HJ (2014) An overview on twin support vector machines. Artif Intell Rev 42(2):245–252CrossRef
25.
Zurück zum Zitat Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962–968CrossRef Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962–968CrossRef
26.
Zurück zum Zitat Tian YJ, Ju XC, Qi ZQ, Shi Y (2013) Improved twin support vector machine. Sci China Math 57(2):417–432CrossRef Tian YJ, Ju XC, Qi ZQ, Shi Y (2013) Improved twin support vector machine. Sci China Math 57(2):417–432CrossRef
27.
Zurück zum Zitat Tian YJ, Qi ZQ, Ju XC, Shi Y, Liu XH (2013) Nonparallel support vector machines for pattern classification. IEEE Trans Cybern 44(7):1067–1079CrossRef Tian YJ, Qi ZQ, Ju XC, Shi Y, Liu XH (2013) Nonparallel support vector machines for pattern classification. IEEE Trans Cybern 44(7):1067–1079CrossRef
28.
Zurück zum Zitat Guarracino MR, Cifarelli C, Seref O, Pardalos PM (2007) A classification method based on generalized eigenvalue problems. Optim Methods Softw 22(1):73–81CrossRef Guarracino MR, Cifarelli C, Seref O, Pardalos PM (2007) A classification method based on generalized eigenvalue problems. Optim Methods Softw 22(1):73–81CrossRef
29.
Zurück zum Zitat Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. The John Hopkins University Press, Baltimore Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. The John Hopkins University Press, Baltimore
30.
Zurück zum Zitat Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings KDD-2001: knowledge discovery and data mining, San Francisco, CA, pp 77–86 Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings KDD-2001: knowledge discovery and data mining, San Francisco, CA, pp 77–86
31.
Zurück zum Zitat Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods—support vector learning. MIT Press, Cambridge, pp 185–208 Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods—support vector learning. MIT Press, Cambridge, pp 185–208
32.
Zurück zum Zitat Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10(5):1032–1037CrossRef Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10(5):1032–1037CrossRef
33.
Zurück zum Zitat Shao YH, Deng NY (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25:114–121CrossRef Shao YH, Deng NY (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25:114–121CrossRef
35.
Zurück zum Zitat Peng XJ (2010) A \(\nu \)-twin support vector machine (\(\nu \)-TSVM) classifier and its geometric algorithms. Inf Sci 180:3863–3875CrossRef Peng XJ (2010) A \(\nu \)-twin support vector machine (\(\nu \)-TSVM) classifier and its geometric algorithms. Inf Sci 180:3863–3875CrossRef
36.
37.
Zurück zum Zitat Xu YT, Wang LS, Zhong P (2012) A rough margin-based v-twin support vector machine. Neural Comput Appl 21(6):1307–1317CrossRef Xu YT, Wang LS, Zhong P (2012) A rough margin-based v-twin support vector machine. Neural Comput Appl 21(6):1307–1317CrossRef
38.
Zurück zum Zitat Peng X (2011) TPMSVM: a novel twin parametric-margin support vector for pattern recognition. Pattern Recogn 44(10–11):2678–2692CrossRef Peng X (2011) TPMSVM: a novel twin parametric-margin support vector for pattern recognition. Pattern Recogn 44(10–11):2678–2692CrossRef
39.
Zurück zum Zitat Hao PY (2010) New support vector algorithms with parametric insensitive margin model. Neural Netw 23(1):60–73CrossRef Hao PY (2010) New support vector algorithms with parametric insensitive margin model. Neural Netw 23(1):60–73CrossRef
40.
Zurück zum Zitat Shao YH, Wang Z, Chen WJ, Deng NY (2013) Least squares twin parametric-margin support vector machine for classification. Appl Intell 39:451–464CrossRef Shao YH, Wang Z, Chen WJ, Deng NY (2013) Least squares twin parametric-margin support vector machine for classification. Appl Intell 39:451–464CrossRef
41.
Zurück zum Zitat Wang Z, Shao YH, Wu TR (2013) A GA-based model selection for smooth twin parametric-margin support vector machine. Pattern Recogn 46(8):2267–2277CrossRef Wang Z, Shao YH, Wu TR (2013) A GA-based model selection for smooth twin parametric-margin support vector machine. Pattern Recogn 46(8):2267–2277CrossRef
42.
Zurück zum Zitat Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535–7543CrossRef Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535–7543CrossRef
43.
Zurück zum Zitat Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific Pub. Co., SingaporeCrossRef Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002) Least squares support vector machines. World Scientific Pub. Co., SingaporeCrossRef
44.
Zurück zum Zitat Chen J, Ji GG (2010) Weighted least squares twin support vector machines for pattern classification, vol. 2. In: The 2nd international conference on computer and automation engineering, pp 242–246 Chen J, Ji GG (2010) Weighted least squares twin support vector machines for pattern classification, vol. 2. In: The 2nd international conference on computer and automation engineering, pp 242–246
45.
Zurück zum Zitat Tomar D, Singhal S, Agarwal S (2014) Weighted least square twin support vector machine for imbalanced dataset. Int J Database Theory Appl 7(2):25–36 Tomar D, Singhal S, Agarwal S (2014) Weighted least square twin support vector machine for imbalanced dataset. Int J Database Theory Appl 7(2):25–36
46.
Zurück zum Zitat Gao SB, Ye QL, Ye N (2011) 1-norm least squares twin support vector machines. Neurocomputing 74(17):3590–3597CrossRef Gao SB, Ye QL, Ye N (2011) 1-norm least squares twin support vector machines. Neurocomputing 74(17):3590–3597CrossRef
47.
Zurück zum Zitat Xu Y, Xi W, Lv X, Guo R (2012) An improved least squares twin support vector machine. J Inf Comput Sci 9:1063–1071 Xu Y, Xi W, Lv X, Guo R (2012) An improved least squares twin support vector machine. J Inf Comput Sci 9:1063–1071
48.
Zurück zum Zitat Peng XJ, Xu D (2013) A twin-hypersphere support vector machine classifier and the fast learning algorithm. Inf Sci 12–27 Peng XJ, Xu D (2013) A twin-hypersphere support vector machine classifier and the fast learning algorithm. Inf Sci 12–27
49.
Zurück zum Zitat Peng XJ, Xu D (2014) Twin support vector hypersphere (TSVH) classifier for pattern recognition. Neural Comput Appl 24(5):1207–1220CrossRef Peng XJ, Xu D (2014) Twin support vector hypersphere (TSVH) classifier for pattern recognition. Neural Comput Appl 24(5):1207–1220CrossRef
50.
Zurück zum Zitat Peng XJ (2010) Least squares twin support vector hypersphere (LS-TSVH) for pattern recognition. Expert Syst Appl 37(12):8371–8378CrossRef Peng XJ (2010) Least squares twin support vector hypersphere (LS-TSVH) for pattern recognition. Expert Syst Appl 37(12):8371–8378CrossRef
51.
Zurück zum Zitat Tax D, Duin R (2004) Support vector data description. Mach Learn 54:45–66CrossRef Tax D, Duin R (2004) Support vector data description. Mach Learn 54:45–66CrossRef
52.
Zurück zum Zitat Chen XB, Yang J, Ye QL, Liang J (2011) Recursive projection twin support vector machine via within-class variance minimization. Pattern Recogn 44(10–11):2643–2655CrossRef Chen XB, Yang J, Ye QL, Liang J (2011) Recursive projection twin support vector machine via within-class variance minimization. Pattern Recogn 44(10–11):2643–2655CrossRef
53.
Zurück zum Zitat Shao YH, Wang Z, Chen WJ, Deng NY (2013) A regularization for the projection twin support vector machine. Knowl-Based Syst 37:203–210CrossRef Shao YH, Wang Z, Chen WJ, Deng NY (2013) A regularization for the projection twin support vector machine. Knowl-Based Syst 37:203–210CrossRef
54.
Zurück zum Zitat Hua XP, Ding SF (2012) Matrix pattern based projection twin support vector machines. Int J Digital Content Technol Appl 6(20):172–181CrossRef Hua XP, Ding SF (2012) Matrix pattern based projection twin support vector machines. Int J Digital Content Technol Appl 6(20):172–181CrossRef
55.
Zurück zum Zitat Shao YH, Deng NY, Yang ZM (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recogn 45(6):2299–2307CrossRef Shao YH, Deng NY, Yang ZM (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recogn 45(6):2299–2307CrossRef
56.
Zurück zum Zitat Ding SF, Hua XP (2014) Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing 130:3–9CrossRef Ding SF, Hua XP (2014) Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing 130:3–9CrossRef
58.
Zurück zum Zitat Ghorai S, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89(4):510–522CrossRef Ghorai S, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89(4):510–522CrossRef
59.
Zurück zum Zitat Ye QL, Zhao CX, Gao SB, Zheng H (2012) Weighted twin support vector machines with local information and its application. Neural Netw 35:31–39CrossRef Ye QL, Zhao CX, Gao SB, Zheng H (2012) Weighted twin support vector machines with local information and its application. Neural Netw 35:31–39CrossRef
60.
Zurück zum Zitat Ye QL, Zhao CX, Ye N, Chen XB (2011) Localized twin SVM via convex minimization. Neurocomputing 74(4):580–587CrossRef Ye QL, Zhao CX, Ye N, Chen XB (2011) Localized twin SVM via convex minimization. Neurocomputing 74(4):580–587CrossRef
61.
Zurück zum Zitat Wang YN, Tian YJ (2012) Fast localized twin SVM. In: 8th international conference on natural computation, pp 74–78 Wang YN, Tian YJ (2012) Fast localized twin SVM. In: 8th international conference on natural computation, pp 74–78
62.
Zurück zum Zitat Wang YN, Zhao X, Tian YJ (2013) Local and global regularized twin SVM, vol. 18. In: International conference on computational science, pp 1710–1719 Wang YN, Zhao X, Tian YJ (2013) Local and global regularized twin SVM, vol. 18. In: International conference on computational science, pp 1710–1719
63.
Zurück zum Zitat Peng XJ, Xu D (2013) Bi-density twin support vector machines for pattern recognition. Neurocomputing 99:134–143CrossRef Peng XJ, Xu D (2013) Bi-density twin support vector machines for pattern recognition. Neurocomputing 99:134–143CrossRef
64.
Zurück zum Zitat Wang D, Ye QL, Ye N (2010) Localized multi-plane twsvm classifier via manifold regularization, vol. 2. In: International conference on intelligent human–machine systems and cybernetics, pp 70–73 Wang D, Ye QL, Ye N (2010) Localized multi-plane twsvm classifier via manifold regularization, vol. 2. In: International conference on intelligent human–machine systems and cybernetics, pp 70–73
65.
Zurück zum Zitat Ye QL, Zhao CX, Ye N (2012) Least squares twin support vector machine classification via maximum one-class within class variance. Optim Methods Softw 27(1):53–69CrossRef Ye QL, Zhao CX, Ye N (2012) Least squares twin support vector machine classification via maximum one-class within class variance. Optim Methods Softw 27(1):53–69CrossRef
66.
Zurück zum Zitat Tian YJ, Ju XC, Qi ZQ (2013) Efficient sparse nonparallel support vector machines for classification. Neural Comput Appl 24(5):1089–1099CrossRef Tian YJ, Ju XC, Qi ZQ (2013) Efficient sparse nonparallel support vector machines for classification. Neural Comput Appl 24(5):1089–1099CrossRef
67.
Zurück zum Zitat Peng XJ (2011) Building sparse twin support vector machine classifiers in primal space. Inf Sci 181(18):3967–3980CrossRef Peng XJ (2011) Building sparse twin support vector machine classifiers in primal space. Inf Sci 181(18):3967–3980CrossRef
68.
Zurück zum Zitat Tanveer M (2013) Smoothing technique on linear programming twin support vector machines. Int J Mach Learn Comput 3(2):240–244CrossRef Tanveer M (2013) Smoothing technique on linear programming twin support vector machines. Int J Mach Learn Comput 3(2):240–244CrossRef
70.
Zurück zum Zitat Qi ZQ, Tian YJ, Shi Y (2013) Structural twin support vector machine for classification. Knowl-Based Syst 43:74–81CrossRef Qi ZQ, Tian YJ, Shi Y (2013) Structural twin support vector machine for classification. Knowl-Based Syst 43:74–81CrossRef
71.
Zurück zum Zitat Kzhuang KH, Yang H, King I (2004) Learning large margin classifiers locally and globally. In: The twenty-first international conference on machine learning (ICML-2004), pp 401–408 Kzhuang KH, Yang H, King I (2004) Learning large margin classifiers locally and globally. In: The twenty-first international conference on machine learning (ICML-2004), pp 401–408
72.
Zurück zum Zitat Xue H, Chen S, Yang Q (2011) Structural regularized support vector machine: a framework for structural large margin classifier. IEEE Trans Neural Netw 22(4):573–587CrossRef Xue H, Chen S, Yang Q (2011) Structural regularized support vector machine: a framework for structural large margin classifier. IEEE Trans Neural Netw 22(4):573–587CrossRef
73.
Zurück zum Zitat Peng XJ, Xu D (2013) Robust minimum class variance twin support vector machine classifier. Neural Comput Appl 22(5):999–1011CrossRef Peng XJ, Xu D (2013) Robust minimum class variance twin support vector machine classifier. Neural Comput Appl 22(5):999–1011CrossRef
74.
Zurück zum Zitat Peng XJ, Xu D (2012) Twin Mahalanobis distance-based support vector machines for pattern recognition. Inf Sci 200:22–37CrossRef Peng XJ, Xu D (2012) Twin Mahalanobis distance-based support vector machines for pattern recognition. Inf Sci 200:22–37CrossRef
75.
Zurück zum Zitat Peng XJ, Wang YF, Xu D (2013) Structural twin parametric margin support vector machine for binary classification. Knowl-Based Syst 49:63–72CrossRef Peng XJ, Wang YF, Xu D (2013) Structural twin parametric margin support vector machine for binary classification. Knowl-Based Syst 49:63–72CrossRef
76.
Zurück zum Zitat Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recogn Lett 29(13):1842–1848CrossRef Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recogn Lett 29(13):1842–1848CrossRef
77.
Zurück zum Zitat Shao YH, Deng NY (2013) A novel margin-based twin support vector machine with unity norm hyperplanes. Neural Comput Appl 22:1627–1635CrossRef Shao YH, Deng NY (2013) A novel margin-based twin support vector machine with unity norm hyperplanes. Neural Comput Appl 22:1627–1635CrossRef
78.
Zurück zum Zitat Ghorai S, Hossian SJ, Mukherjee A, Dutta PK (2010) Unity norm twin support vector machine classifier. In: Annual IEEE India conference, pp 1–4 Ghorai S, Hossian SJ, Mukherjee A, Dutta PK (2010) Unity norm twin support vector machine classifier. In: Annual IEEE India conference, pp 1–4
79.
Zurück zum Zitat Peng XJ, Xu D (2013) Norm-mixed twin support vector machine classifier and its geometric algorithm. Neurocomputing 99:486–495CrossRef Peng XJ, Xu D (2013) Norm-mixed twin support vector machine classifier and its geometric algorithm. Neurocomputing 99:486–495CrossRef
80.
Zurück zum Zitat Bai L, Wang Z, Shao YH, Deng NY (2014) A novel feature selection method for twin support vector machine. Knowl-Based Systems 59:1–8CrossRef Bai L, Wang Z, Shao YH, Deng NY (2014) A novel feature selection method for twin support vector machine. Knowl-Based Systems 59:1–8CrossRef
81.
Zurück zum Zitat Ye QL, Zhao CX, Ye N, Zheng H, Chen XB (2012) A feature selection method for nonparallel plane support vector machine classification. Optim Methods Softw 27(3):431–443CrossRef Ye QL, Zhao CX, Ye N, Zheng H, Chen XB (2012) A feature selection method for nonparallel plane support vector machine classification. Optim Methods Softw 27(3):431–443CrossRef
82.
Zurück zum Zitat Khemchandani R, Jayadeva, Chandra S (2009) Optimal kernel selection in twin support vector machines. Optim Lett 3(1):77–88CrossRef Khemchandani R, Jayadeva, Chandra S (2009) Optimal kernel selection in twin support vector machines. Optim Lett 3(1):77–88CrossRef
83.
Zurück zum Zitat Shao YH, Deng NY, Yang ZM, Chen WJ, Wang Z (2012) Probabilistic outputs for twin support vector machines. Knowl-Based Syst 33:145–151 Shao YH, Deng NY, Yang ZM, Chen WJ, Wang Z (2012) Probabilistic outputs for twin support vector machines. Knowl-Based Syst 33:145–151
84.
Zurück zum Zitat Shao YH, Chen WJ, Zhang JJ, Wang Z, Deng NY (2014) An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recogn 47:3158–3167CrossRef Shao YH, Chen WJ, Zhang JJ, Wang Z, Deng NY (2014) An efficient weighted Lagrangian twin support vector machine for imbalanced data classification. Pattern Recogn 47:3158–3167CrossRef
85.
Zurück zum Zitat Shao YH, Chen WJ, Deng NY (2014) Nonparallel hyperplane support vector machine for binary classification problems. Inf Sci 263:22–35CrossRef Shao YH, Chen WJ, Deng NY (2014) Nonparallel hyperplane support vector machine for binary classification problems. Inf Sci 263:22–35CrossRef
86.
Zurück zum Zitat Zhang XS, Gao XB, Wang Y (2009) Twin support tensor machines for MCS detection. J Electron (China) 26:318–325CrossRef Zhang XS, Gao XB, Wang Y (2009) Twin support tensor machines for MCS detection. J Electron (China) 26:318–325CrossRef
87.
Zurück zum Zitat Zhao XB, Shi HF, Lv M, Jing L (2014) Least squares twin support tensor machine for classification. J Inf Comput Sci 11(12):4175–4189CrossRef Zhao XB, Shi HF, Lv M, Jing L (2014) Least squares twin support tensor machine for classification. J Inf Comput Sci 11(12):4175–4189CrossRef
88.
Zurück zum Zitat Cai D, He XF, Wen JR, Han J, Ma WY (2006) Support tensor machines for text categorization. Department of Computer Science Technical Report No. 2714, University of Illinois at Urbana—Champaign (UIUCDCS-R-2006-2714) Cai D, He XF, Wen JR, Han J, Ma WY (2006) Support tensor machines for text categorization. Department of Computer Science Technical Report No. 2714, University of Illinois at Urbana—Champaign (UIUCDCS-R-2006-2714)
89.
Zurück zum Zitat Kotsia I, Patras I (2011) Support tucker machines. In: Proceedings of IEEE conference on computer vision and pattern recognition, Colorado, USA, pp 633–640 Kotsia I, Patras I (2011) Support tucker machines. In: Proceedings of IEEE conference on computer vision and pattern recognition, Colorado, USA, pp 633–640
90.
Zurück zum Zitat Kotsia I, Guo WW, Patras I (2012) Higher rank support tensor machines for visual recognition. Pattern Recogn 45:4192–4203CrossRef Kotsia I, Guo WW, Patras I (2012) Higher rank support tensor machines for visual recognition. Pattern Recogn 45:4192–4203CrossRef
91.
Zurück zum Zitat Peng XJ (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365–372CrossRef Peng XJ (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365–372CrossRef
92.
Zurück zum Zitat Zhao YP, Zhao J, Zhao M (2013) Twin least squares support vector regression. Neurocomputing 118:225–236CrossRef Zhao YP, Zhao J, Zhao M (2013) Twin least squares support vector regression. Neurocomputing 118:225–236CrossRef
93.
Zurück zum Zitat Zhong P, Xu YT, Zhao YH (2012) Training twin support vector regression via linear programming. Neural Comput Appl 21(2):399–407CrossRef Zhong P, Xu YT, Zhao YH (2012) Training twin support vector regression via linear programming. Neural Comput Appl 21(2):399–407CrossRef
94.
Zurück zum Zitat Xu YT, Wang LS (2012) A weighted twin support vector regression. Knowl-Based Syst 33:92–101CrossRef Xu YT, Wang LS (2012) A weighted twin support vector regression. Knowl-Based Syst 33:92–101CrossRef
95.
Zurück zum Zitat Shao YH, Zhang CH, Yang ZM, Jing L, Deng NY (2013) \(\varepsilon \)-twin support vector machine for regression. Neural Comput Appl 23(1):175–185 Shao YH, Zhang CH, Yang ZM, Jing L, Deng NY (2013) \(\varepsilon \)-twin support vector machine for regression. Neural Comput Appl 23(1):175–185
96.
97.
Zurück zum Zitat Peng XJ (2012) Efficient twin parametric insensitive support vector regression model. Neurocomputing 79:26–38CrossRef Peng XJ (2012) Efficient twin parametric insensitive support vector regression model. Neurocomputing 79:26–38CrossRef
98.
Zurück zum Zitat Chen XB, Yang J, Liang J, Ye QL (2012) Smooth twin support vector regression. Neural Comput Appl 21(3):505–513CrossRef Chen XB, Yang J, Liang J, Ye QL (2012) Smooth twin support vector regression. Neural Comput Appl 21(3):505–513CrossRef
99.
Zurück zum Zitat Peng XJ (2010) Primal twin support vector regression and its sparse approximation. Neurocomputing 73(16–18):2846–2858CrossRef Peng XJ (2010) Primal twin support vector regression and its sparse approximation. Neurocomputing 73(16–18):2846–2858CrossRef
100.
Zurück zum Zitat Huang HJ, Ding SF, Shi ZZ (2013) Primal least squares twin support vector regression. J Zhejiang Univ Sci C 14(9):722–732CrossRef Huang HJ, Ding SF, Shi ZZ (2013) Primal least squares twin support vector regression. J Zhejiang Univ Sci C 14(9):722–732CrossRef
101.
Zurück zum Zitat Balasundaram S, Tanveer M (2013) On Lagrangian twin support vector regression. Neural Comput Appl 22(1):257–267CrossRef Balasundaram S, Tanveer M (2013) On Lagrangian twin support vector regression. Neural Comput Appl 22(1):257–267CrossRef
102.
Zurück zum Zitat Singh M, Chadha J, Ahuja P, Jayadeva, Chandra S (2011) Reduced twin support vector regression. Neurocomputing 74(9):1471–1477CrossRef Singh M, Chadha J, Ahuja P, Jayadeva, Chandra S (2011) Reduced twin support vector regression. Neurocomputing 74(9):1471–1477CrossRef
103.
Zurück zum Zitat Khemchandani R, Karpatne A, Chandra Suresh (2013) Twin support vector regression for the simultaneous learning of a function and its derivatives. Int J Mach Learn Cybern 4(1):51–63CrossRef Khemchandani R, Karpatne A, Chandra Suresh (2013) Twin support vector regression for the simultaneous learning of a function and its derivatives. Int J Mach Learn Cybern 4(1):51–63CrossRef
104.
Zurück zum Zitat Jayadeva, Khemchandani R, Chandra S (2006) Regularized least squares twin svr for the simultaneous learning of a function and its derivative. In: 2006 international joint conference on neural networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, pp 1192–1197 Jayadeva, Khemchandani R, Chandra S (2006) Regularized least squares twin svr for the simultaneous learning of a function and its derivative. In: 2006 international joint conference on neural networks Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, pp 1192–1197
105.
Zurück zum Zitat Allwein EL, Schapire RE (2001) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113–141 Allwein EL, Schapire RE (2001) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113–141
106.
Zurück zum Zitat Yang Z, Shao Y, Zhang X (2013) Multiple birth support vector machine for multiclass classification. Neural Comput Appl 22(1):153–161CrossRef Yang Z, Shao Y, Zhang X (2013) Multiple birth support vector machine for multiclass classification. Neural Comput Appl 22(1):153–161CrossRef
107.
Zurück zum Zitat Wu ZD, Yang CF (2009) Study to multi-twin support vector machines and its applications in speaker recognition. In: International conference on computational intelligence and software engineering, pp 1–4 Wu ZD, Yang CF (2009) Study to multi-twin support vector machines and its applications in speaker recognition. In: International conference on computational intelligence and software engineering, pp 1–4
108.
Zurück zum Zitat Zhen W, Jin C, Ming Q (2010) Non-parallel planes support vector machine for multi-class classification. Int Conf Logistics Syst Intell Manag 1:581–585 Zhen W, Jin C, Ming Q (2010) Non-parallel planes support vector machine for multi-class classification. Int Conf Logistics Syst Intell Manag 1:581–585
109.
Zurück zum Zitat Jayadeva R, Khemchandai, Chandra S (2007) Fuzzy multi-category proximal support vector classification via generalized eigenvalues. Soft Comput 11(7):679–685CrossRef Jayadeva R, Khemchandai, Chandra S (2007) Fuzzy multi-category proximal support vector classification via generalized eigenvalues. Soft Comput 11(7):679–685CrossRef
110.
Zurück zum Zitat Shao YH, Chen WJ, Huang WB, Yang ZM, Deng NY (2013) The best separating decision tree twin support vector machine for multi-class classification. Proc Comput Sci 17:1032–1038CrossRef Shao YH, Chen WJ, Huang WB, Yang ZM, Deng NY (2013) The best separating decision tree twin support vector machine for multi-class classification. Proc Comput Sci 17:1032–1038CrossRef
111.
Zurück zum Zitat Angulo C, Parra X, Catal A (2003) K-SVCR: a support vector machine for multi-class classification. Neurocomputing 55:57–77CrossRef Angulo C, Parra X, Catal A (2003) K-SVCR: a support vector machine for multi-class classification. Neurocomputing 55:57–77CrossRef
112.
Zurück zum Zitat Xu YT, Guo R, Wang LS (2013) A twin multi-class classification support vector machine. Cogn Comput 5(4):580–588CrossRef Xu YT, Guo R, Wang LS (2013) A twin multi-class classification support vector machine. Cogn Comput 5(4):580–588CrossRef
113.
Zurück zum Zitat Chen J, Ji GG (2010) Multi-class lstsvm classifier based on optimal directed acyclic graph, vol. 3. In: The 2nd international conference on computer and automation engineering, pp 100–104 Chen J, Ji GG (2010) Multi-class lstsvm classifier based on optimal directed acyclic graph, vol. 3. In: The 2nd international conference on computer and automation engineering, pp 100–104
114.
Zurück zum Zitat Zhu XJ (2006) Semi-supervised learning literature survey. Computer Sciences TR 1530, University of Wisconsin Zhu XJ (2006) Semi-supervised learning literature survey. Computer Sciences TR 1530, University of Wisconsin
115.
Zurück zum Zitat Belkin M, Niyogi PP, Sindhwani VV (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434 Belkin M, Niyogi PP, Sindhwani VV (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434
116.
Zurück zum Zitat Qi ZQ, Tian YJ, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35:46–53CrossRef Qi ZQ, Tian YJ, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35:46–53CrossRef
117.
Zurück zum Zitat Chen WJ, Shao YH, Ning H (2014) Laplacian smooth twin support vector machine for semi-supervised classification. Int J Mach Learn Cybern 5(3):459–468CrossRef Chen WJ, Shao YH, Ning H (2014) Laplacian smooth twin support vector machine for semi-supervised classification. Int J Mach Learn Cybern 5(3):459–468CrossRef
118.
Zurück zum Zitat Geurts P (2011) Learning from positive and unlabeled examples by enforcing statistical significance. Int Conf Artif Intell Stat 15:305–314 Geurts P (2011) Learning from positive and unlabeled examples by enforcing statistical significance. Int Conf Artif Intell Stat 15:305–314
119.
Zurück zum Zitat Liu B (2006) Web data mining: exploring hyperplinks, contents, and usage data. Springer, Berlin Liu B (2006) Web data mining: exploring hyperplinks, contents, and usage data. Springer, Berlin
120.
121.
Zurück zum Zitat Zhang Y, Tian YJ, Ju XC (2014) Nonparallel hyperplane support vector machine for pu learning. In: The 2014 10th international conference on natural computation (ICNC 2014) Zhang Y, Tian YJ, Ju XC (2014) Nonparallel hyperplane support vector machine for pu learning. In: The 2014 10th international conference on natural computation (ICNC 2014)
122.
Zurück zum Zitat Weston J, Collobert R, Sinz F, Bottou LL, Vapnik V (2006) Inference with the universum. In: Proceedings of the 23rd international conference on machine learning, pp 1009–1016 Weston J, Collobert R, Sinz F, Bottou LL, Vapnik V (2006) Inference with the universum. In: Proceedings of the 23rd international conference on machine learning, pp 1009–1016
123.
Zurück zum Zitat Qi ZQ, Tian YJ, Shi Y (2012) Twin support vector machine with universum data. Neural Netw 36:112–119CrossRef Qi ZQ, Tian YJ, Shi Y (2012) Twin support vector machine with universum data. Neural Netw 36:112–119CrossRef
124.
Zurück zum Zitat Lu SX, Tong L (2014) Weighted twin support vector machine with universum. Adv Comput Sci 3(2):17–23 Lu SX, Tong L (2014) Weighted twin support vector machine with universum. Adv Comput Sci 3(2):17–23
125.
Zurück zum Zitat Qi ZQ, Tian YJ, Shi Y (2014) A nonparallel support vector machine for a classification problem with universum learning. J Comput Appl Math 263:288–298CrossRef Qi ZQ, Tian YJ, Shi Y (2014) A nonparallel support vector machine for a classification problem with universum learning. J Comput Appl Math 263:288–298CrossRef
126.
Zurück zum Zitat Vapnik V, Vashist A (2009) A new learning paradigm: learning using privileged information. Neural Netw 22:544–557CrossRef Vapnik V, Vashist A (2009) A new learning paradigm: learning using privileged information. Neural Netw 22:544–557CrossRef
127.
Zurück zum Zitat Qi ZQ, Tian YJ, Shi Y (2014) A new classification model using privileged information and itsapplication. Neurocomputing 129:146–152CrossRef Qi ZQ, Tian YJ, Shi Y (2014) A new classification model using privileged information and itsapplication. Neurocomputing 129:146–152CrossRef
128.
Zurück zum Zitat Pannagadatta SK, Bhattacharyya C, Smola AJ (2006) Second order cone programming approaches for handling missing and uncertain data. J Mach Learn Res 7:1283–1314 Pannagadatta SK, Bhattacharyya C, Smola AJ (2006) Second order cone programming approaches for handling missing and uncertain data. J Mach Learn Res 7:1283–1314
129.
Zurück zum Zitat Zhong P, Fukushima M (2007) Second order cone programming formulations for robust multi-classclassification. Neural Comput 19(1):258–282CrossRef Zhong P, Fukushima M (2007) Second order cone programming formulations for robust multi-classclassification. Neural Comput 19(1):258–282CrossRef
130.
Zurück zum Zitat Qi ZQ, Tian YJ, Shi Y (2013) Robust twin support vector machine for pattern classification. Pattern Recognit 46(1):305–316CrossRef Qi ZQ, Tian YJ, Shi Y (2013) Robust twin support vector machine for pattern classification. Pattern Recognit 46(1):305–316CrossRef
131.
Zurück zum Zitat Kumara MA, Khemchandanic R, Gopala M, Chandrad S (2010) Knowledge based least squares twin support vector machines. Inf Sci 180(23):4606–4618CrossRef Kumara MA, Khemchandanic R, Gopala M, Chandrad S (2010) Knowledge based least squares twin support vector machines. Inf Sci 180(23):4606–4618CrossRef
132.
Zurück zum Zitat Ju XC, Tian YJ (2011) A novel knowledge-based twin support vector machine. International conference on data mining workshops, pp 429–433 Ju XC, Tian YJ (2011) A novel knowledge-based twin support vector machine. International conference on data mining workshops, pp 429–433
133.
Zurück zum Zitat Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple instance learning. In: Neural information processing systems, pp 561–568 Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple instance learning. In: Neural information processing systems, pp 561–568
134.
Zurück zum Zitat Yang ZX, Deng NY (2009) Multi-instance support vector machine based on convex combination. In: The eighth international symposium on operations research and its applications, pp 481–487 Yang ZX, Deng NY (2009) Multi-instance support vector machine based on convex combination. In: The eighth international symposium on operations research and its applications, pp 481–487
135.
Zurück zum Zitat Qi ZQ, Tian YJ, Yu XD, Shi Y (2014) A multi-instance learning algorithm based on nonparallel classifier. Appl Math Comput 241:233–241CrossRef Qi ZQ, Tian YJ, Yu XD, Shi Y (2014) A multi-instance learning algorithm based on nonparallel classifier. Appl Math Comput 241:233–241CrossRef
136.
Zurück zum Zitat Shao YH, Yang ZX, Wang XB, NY (2010) Multiple instance twin support vector machines. The ninth international symposium on operations research and its applications, pp 433–442 Shao YH, Yang ZX, Wang XB, NY (2010) Multiple instance twin support vector machines. The ninth international symposium on operations research and its applications, pp 433–442
137.
Zurück zum Zitat Zhang Q, Tian YJ, Liu DL (2013) Nonparallel support vector machines for multiple-instance learning. Procedia Comput Sci 17:1063–1072CrossRef Zhang Q, Tian YJ, Liu DL (2013) Nonparallel support vector machines for multiple-instance learning. Procedia Comput Sci 17:1063–1072CrossRef
138.
Zurück zum Zitat Liu LY, Zhao YH, Zhong P (2012) Multiple instance classification based on least squares twin support vector machine. J Converg Inf Technol 7(6):72–77CrossRef Liu LY, Zhao YH, Zhong P (2012) Multiple instance classification based on least squares twin support vector machine. J Converg Inf Technol 7(6):72–77CrossRef
139.
140.
Zurück zum Zitat Evgeniou T, Micchelli C, Pontil M (2006) Learning multiple tasks with kernel methods. J Mach Learn Res 6(1):615–623 Evgeniou T, Micchelli C, Pontil M (2006) Learning multiple tasks with kernel methods. J Mach Learn Res 6(1):615–623
141.
Zurück zum Zitat Evgeniou T, Pontil M (2004) Regularized multictask learning. In: KDD, pp 109–117 Evgeniou T, Pontil M (2004) Regularized multictask learning. In: KDD, pp 109–117
142.
Zurück zum Zitat Xie XJ, Sun SL (2012) Multitask twin support vector machines. Neural Inf Process 7664:341–348CrossRef Xie XJ, Sun SL (2012) Multitask twin support vector machines. Neural Inf Process 7664:341–348CrossRef
143.
Zurück zum Zitat Joachims T (1998) Making large-scale svm learning practical. In: Advances in kernel methods-support vector learning, MIT Press, Cambridge, pp 169–184 Joachims T (1998) Making large-scale svm learning practical. In: Advances in kernel methods-support vector learning, MIT Press, Cambridge, pp 169–184
144.
Zurück zum Zitat Chang CC, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27CrossRef Chang CC, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27CrossRef
145.
Zurück zum Zitat Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874 Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874
146.
Zurück zum Zitat Joachims T (2006) Training linear svms in linear time. In In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD’06, ACM, New York, pp 217–226 Joachims T (2006) Training linear svms in linear time. In In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD’06, ACM, New York, pp 217–226
147.
Zurück zum Zitat Hsieh CJ, Chang KW, Lin CJ, Keerthi SS, Sundararajan S (2008) A dual coordinate descent method for large-scale linear svm. In Proceedings of the 25th international conference on machine learning, ICML ’08, ACM, New York, pp 408–415, Hsieh CJ, Chang KW, Lin CJ, Keerthi SS, Sundararajan S (2008) A dual coordinate descent method for large-scale linear svm. In Proceedings of the 25th international conference on machine learning, ICML ’08, ACM, New York, pp 408–415,
148.
Zurück zum Zitat Tian YJ, Ping Y (2014) Large-scale linear nonparallel support vector machine solver. Neural Netw 50:166–174CrossRef Tian YJ, Ping Y (2014) Large-scale linear nonparallel support vector machine solver. Neural Netw 50:166–174CrossRef
149.
Zurück zum Zitat Tian YJ, Zhang Q, Ping Y (2014) Large-scale linear nonparallel support vector machine solver. Neurocomputing 138:114–119CrossRef Tian YJ, Zhang Q, Ping Y (2014) Large-scale linear nonparallel support vector machine solver. Neurocomputing 138:114–119CrossRef
150.
Zurück zum Zitat Cong HH, Yang CF, XRP (2008) Efficient speaker recognition based on multi-class twin support vector machines and gmms. In: 2008 IEEE conference on robotics, automation and mechatronics, pp 348–352 Cong HH, Yang CF, XRP (2008) Efficient speaker recognition based on multi-class twin support vector machines and gmms. In: 2008 IEEE conference on robotics, automation and mechatronics, pp 348–352
151.
Zurück zum Zitat Liu M, Xie Y, Yao Z, Dai B (2006) A new hybrid gmm/svmfor speaker verification. In: International conference on pattern recognition (ICPR’06) Liu M, Xie Y, Yao Z, Dai B (2006) A new hybrid gmm/svmfor speaker verification. In: International conference on pattern recognition (ICPR’06)
152.
Zurück zum Zitat Naik GR, Kumar DK, Jayadeva (2010) Twin svm for gesture classification using the surface electromyogram. IEEE Trans Inf Technol Biomed 14(2):301–308CrossRef Naik GR, Kumar DK, Jayadeva (2010) Twin svm for gesture classification using the surface electromyogram. IEEE Trans Inf Technol Biomed 14(2):301–308CrossRef
153.
Zurück zum Zitat Mozafari K, Nasiri JA, Charkari NM, Jalili S (2011) Action recognition by space-time features and least squares twin svm. In: The first international conference on informatics and computational intelligence, pp 287–292 Mozafari K, Nasiri JA, Charkari NM, Jalili S (2011) Action recognition by space-time features and least squares twin svm. In: The first international conference on informatics and computational intelligence, pp 287–292
154.
Zurück zum Zitat Yang CF, Ji LP, Liu GS (2009) Study to speech emotion recognition based on twinssvm. Fifth Int Conf Nat Comput 2:312–316 Yang CF, Ji LP, Liu GS (2009) Study to speech emotion recognition based on twinssvm. Fifth Int Conf Nat Comput 2:312–316
155.
Zurück zum Zitat Si X, Jing L (2009) Mass detection in digital mammograms using twin support vector machine-based cad system. WASE Int Conf Inf Eng 1:240–243 Si X, Jing L (2009) Mass detection in digital mammograms using twin support vector machine-based cad system. WASE Int Conf Inf Eng 1:240–243
156.
Zurück zum Zitat Zhang XS, Gao XB, Wang Y (2009) Mcs detection with combined image features and twin support vector machines. J Comput 4(3):215–221CrossRef Zhang XS, Gao XB, Wang Y (2009) Mcs detection with combined image features and twin support vector machines. J Comput 4(3):215–221CrossRef
157.
Zurück zum Zitat Zhang XS (2009) Boosting twin support vector machine approach for mcs detection. Asia-Pac Conf Inf Process 1:149–152 Zhang XS (2009) Boosting twin support vector machine approach for mcs detection. Asia-Pac Conf Inf Process 1:149–152
158.
Zurück zum Zitat Tomar D, Agarwal S (2014) Feature selection based least square twin support vector machine for diagnosis of heart disease. Int J Bio-Sci Bio-Technol 6(2):69–82 Tomar D, Agarwal S (2014) Feature selection based least square twin support vector machine for diagnosis of heart disease. Int J Bio-Sci Bio-Technol 6(2):69–82
159.
Zurück zum Zitat Ding XJ, Zhang GL, Ke YZ, Ma BL, Li ZC (2008) High efficient intrusion detection methodology with twin support vector machines. Int Symp Inf Sci Eng 1:560–564 Ding XJ, Zhang GL, Ke YZ, Ma BL, Li ZC (2008) High efficient intrusion detection methodology with twin support vector machines. Int Symp Inf Sci Eng 1:560–564
160.
Zurück zum Zitat Tian YJ, Shi Y, Liu XH (2012) Recent advances on support vector machines research. Technol Econ Develop Econ 18(1):5–33CrossRef Tian YJ, Shi Y, Liu XH (2012) Recent advances on support vector machines research. Technol Econ Develop Econ 18(1):5–33CrossRef
Metadaten
Titel
Review on: Twin Support Vector Machines
verfasst von
Yingjie Tian
Zhiquan Qi
Publikationsdatum
01.06.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 2/2014
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-014-0018-4

Weitere Artikel der Ausgabe 2/2014

Annals of Data Science 2/2014 Zur Ausgabe