K-nearest neighbor-based weighted twin support vector regression

Applied Intelligence

Volumn 41, Issue 1, 2014, Pages 299-309

  Xu, Yitian ^a,b   Wang, Laisheng ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b Arizona State University   (United States)

Author keywords

K nearest neighbor; Successive overrelaxation; TSVR; Weights

Indexed keywords

MEMBERSHIP FUNCTIONS; QUADRATIC PROGRAMMING;

COMPUTATIONAL SPEED; K-NEAREST NEIGHBORS; QUADRATIC PROGRAMMING PROBLEMS; SUCCESSIVE OVER RELAXATION; SUPPORT VECTOR REGRESSION (SVR); TSVR; UP- AND DOWN-BOUND FUNCTIONS; WEIGHTS;

TEXT PROCESSING;

EID: 84904180159     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-014-0518-0     Document Type: Article

References (28)

1. Cai D, He X, Zhang W, Han J (2007) Regularized locality preserving indexing via spectral regression. In: Proc 2007 ACM int. conf. on information and knowledge management
2. Cheng H, Tan P, Jin R (2010) Efficient algorithm for localized support vector machine. IEEE Trans Knowl Data Eng 22(4):537-549
3. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21-27
4. Diosan L, Rogozan A, Pecuchet J-P (2013) Improving classification performance of support vector machine by genetically optimising kernel shape and hyper-parameters. Appl Intell 36:280-294
5. Fung G, Mangasarian O (2001) Proximal support vector machine classifiers. In: Seven international proceedings on knowledge discovery and data mining, pp 77-86
6. Fung G, Mangasarian O (2005) Multicategory proximal support vector machine classifiers. Mach Learn 59:77-97 (Pubitemid 40890413)
7. Ghorai S, Mukherjee A, Dutta P (2009) Nonparallel plane proximal classifier. Signal Process 89(4):510-522
8. Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905-910 (Pubitemid 46534787)
9. Jayadeva, Khemchandani R, Chandra S (2007) Fuzzy multi-category proximal support vector classification via generalized eigenvalues. Soft Comput 11(7):679-685
10. Khemchandani R, Jayadeva, Chandra S (2009) Optimal kernel selection in twin support vector machines. Optim Lett 3(1):77-88
11. Kumar M, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535-7543
12. Li I, Chen J, Wu J (2013) A fast prototype reduction method based on template reduction and visualization-induced self-organizing map for nearest neighbor algorithm. Appl Intell 39:564-582
13. Mangasarian O, Musicant D (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10:1032-1037
14. Matei O, Pop P, Vǎlean (2013) Optical character recognition in real environments using neural networks and k-nearest neighbor. Appl Intell 39:739-748
15. Owusu E, Zhan Y, Mao Q (2013) An SVM-AdaBoost facial expression recognition system. Appl Intell. doi: 10.1007/s10489-013-0478-9
16. Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365-372
17. Peng X (2010) A new twin support vector machine classifier and its geometric algorithms. Inf Sci 180(20):3863-3875
18. Ripley B (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge
19. Shao Y, Wang Z, Chen W, Deng N (2013) Least squares twin parametric-margin support vector machine for classification. Appl Intell 39:451-464
20. Vapnik V (1995) The nature of statistical learning theory. Springer, New York
21. Wang C, You W (2013) Boosting-SVM: effective learning with reduced data dimension. Appl Intell 39:465-474
22. Xu Y (2012) A rough margin-based linear ν support vector regression. Stat Probab Lett 82:528-534
23. Xu Y, Guo R (2014) An improved v-twin support vector machine. Applied Intell. doi: 10.1007/s10489-013-0500-2
24. Xu Y, Wang L (2005) Fault diagnosis system based on rough set theory and support vector machine. Lect Notes Artif Intell 3614:981-988
25. Xu Y, Wang L (2012) A weighted twin support vector regression. Knowl-Based Syst 33:92-101
26. Xu Y, Wang L, Zhong P (2012) A rough margin-based ν-twin support vector machine. Neural Comput Appl 21:1307-1317
27. Xu Y, Lv X, Xi W (2012) A weighted multi-output support vector regression and its application. J Comput Inf Syst 8(9):3807-3814
28. Ye Q, Zhao C, Gao S, Zheng H (2012) Weighted twin support vector machines with local information and its application. Neural Netw 35:31-39