Matrix computations (3rd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Lagrangian support vector machines
The Journal of Machine Learning Research
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Classification in a normalized feature space using support vector machines
IEEE Transactions on Neural Networks
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A new approach for classification problems, called proximal bilateral-weighted fuzzy support vector machine, is proposed wherein each input example is treated as belonging to both positive and negative classes with different fuzzy memberships. The assumption of treating every input example belonging to both the classes is very well justified in real world applications. For example, for the study of credit risk assessment a customer can not always be assumed to be absolutely good or bad as he may default or pay his debit at times and therefore he may be treated as belonging to both the classes. Our formulation leads to solving a system of linear equations of size equals to the number of input examples. Computational results of the proposed method on publicly available datasets including two credit risk analysis datasets to that of the standard, proximal and bilateral-weighted fuzzy support vector machine methods clearly demonstrates its efficiency and usefulness.