References
- BaesensBVan GestelTViaeneSStepanovaMSuykensJVanthienenJBenchmarking state-of-the-art classification algorithms for credit scoringJournal of the Operational Research Society200354662763510.1057/palgrave.jors.2601545
- Bai Y and Yan X (2015). Conic relaxation for semi-supervised support vector machines. Journal of Optimization Theory and Applications. DOI: 10.1007/s10957-015-0843-4.
- BaiYChenYNiuBSDP relaxation for semi-supervised support vector machinePacific Journal of Optimization201281314
- BaiYNiuBChenYNew SDP models for protein homology detection with semi-supervised SVMOptimization201362456157210.1080/02331934.2011.611515
- BennettKDemirizASemi-supervised support vector machinesAdvances in Neural Information Processing Systems 111999368374
- BoylanJSyntetosAAKarakostasGClassification for forecasting and stock control: A case studyJournal of the Operational Research Society200859447348110.1057/palgrave.jors.2602312
- Chapelle O and Zien A (2005). Semi-supervised classification by low density separation. In: Cowell RG and Ghahramani Z (eds). Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, http://www.gatsby.ucl.ac.uk/aistats/, pp 57–64.
- ChapelleOSindhwaniVKeerthiSSOptimization techniques for semi-supervised support vector machinesThe Journal of Machine Learning Research20089Feb203233
- CollobertRSinzFWestonJBottouLLarge scale transductive SVMsThe Journal of Machine Learning Research20067Aug16871712
- CortesCVapnikVSupport-vector networksMachine learning1995203273297
- CristianiniNShawe-TaylorJAn Introduction to Support Vector Machines and Other Kernel-Based Learning Methods2000
- De BieTCristianiniNConvex methods for transductionAdvances in Neural Information Processing Systems 1620047380
- DengNYTianYJZhangCHSupport Vector Machines-Optimization Based Theory, Algorithms and Extensions2012
- Grant M, Boyd S and Ye Y (2015). CVX: Matlab software for disciplined convex programming, version 2.1. Available from http://cvxr.com/cvx/.
- HansenJMcDonaldJNelsonRSome evidence on forecasting time-series with support vector machinesJournal of the Operational Research Society20065791053106310.1057/palgrave.jors.2602073
- IssamDQuadratic kernel-free non-linear support vector machineJournal of Global Optimization2008411153010.1007/s10898-007-9162-0
- Joachims T (1999). Transductive inference for text classification using support vector machines. In: Bratko I and Dzeroski S (eds). Proceedings of 16th International Conference on Machine Learning, Morgan Kaufmann: San Francisco, CA, pp 200–209.
- KennedyKMac NameeBDelanySJUsing semi-supervised classifiers for credit scoringJournal of the Operational Research Society201264451352910.1057/jors.2011.30
- Le HM, Le Thi HA and Nguyen MC (2013). DCA based algorithms for feature selection in semi-supervised support vector machines. In: Perner P (ed). Machine Learning and Data Mining in Pattern Recognition, LNAI 7988, pp 528–542.
- LessmannSBaesensBSeowHVThomasLCBenchmarking state-of-the-art classification algorithms for credit scoring: An update of researchEuropean Journal of Operational Research2015247112413610.1016/j.ejor.2015.05.030
- LiHHandDDirect versus indirect credit scoring classificationsJournal of the Operational Research Society200253664765410.1057/palgrave.jors.2601346
- LuoJFangSCBaiYDengZFuzzy quadratic surface support vector machine based on Fisher discriminant analysisJournal of Industrial and Management Optimization201612135737310.3934/jimo.2016.12.357
- Luo J (2014). Quadratic surface support vector machines with applications. Ph.D. dissertation, North Carolina State University.
- MaldonadoSParedesGA semi-supervised approach for reject inference in credit scoring using SVMsAdvances in Data Mining, Applications and Theoretical Aspects2010558571
- Osuna E, Freund R and Girosi F (1997). Training support vector machines: An application to face detection. In: Medioni G and Nevatia R (eds). IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society: Washington DC, pp 130–136.
- SchebeschKBSteckingRSupport vector machines for classifying and describing credit applicants: Detecting typical and critical regionsJournal of the Operational Research Society20055691082108810.1057/palgrave.jors.2602023
- Sindhwani V, Keerthi SS and Chapelle O (2006). Deterministic annealing for semi-supervised kernel machines. In: Cohen W and Moore A (eds). Proceedings of the 23rd International Conference on Machine Learning, Omni Press: Madison, WI, pp 841–848.
- SunJShangZLiHImbalance-oriented SVM methods for financial distress prediction: A comparative study among the new SB-SVM-ensemble method and traditional methodsJournal of the Operational Research Society201465121905191910.1057/jors.2013.117
- ValizadeganHJinRGeneralized maximum margin clustering and unsupervised kernel learningAdvances in Neural Information Processing Systems 19200614171424
- VapnikNKSterinAOn structural risk minimization or overall risk in a problem of pattern recognitionAutomation and Remote Control197710314951503
- WenZWGoldfarbDYinWTAlternating direction augmented Lagrangian methods for semidefinite programmingMathematical Programming Computation201023–420323010.1007/s12532-010-0017-1
- Xu L and Schuurmans D (2005). Unsupervised and semi-supervised multi-class support vector machines. In: Veloso M and Kambhampati S (eds). Proceedings of the 20th National Conference on Artificial Intelligence, Vol. 20, AAAI Press: Menlo Park, CA, pp 904–910.
- XuZLJinRZhuJKKingILyuMEfficient convex relaxation for transductive support vector machineAdvances in Neural Information Processing Systems 20200816411648
- Zhao B, Wang F and Zhang CS (2008). CutS3VM: A fast semi-supervised SVM algorithm. In: Li Y, Liu B and Sarawagi S (eds). Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM: New York, pp 830–838.