The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Dual /spl nu/-support vector machine with error rate and training size biasing
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Learning approaches for developing successful seller strategies in dynamic supply chain management
Information Sciences: an International Journal
Computers and Operations Research
A hybrid KMV model, random forests and rough set theory approach for credit rating
Knowledge-Based Systems
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This paper deals with the application of support vector machine (SVM) for bond rating. The three commonly used methods for solving multi-class classification problems in SVM, "one-against-all", "one-against-one", and directed acyclic graph SVM (DAGSVM) are used. The performance of SVM is compared with several benchmarks. One real U.S. bond data is collected using the Fixed Investment Securities database (FISD) and the Compustat database. The experiment shows that SVM significantly outperforms the benchmarks. Among the three SVM based methods, there is the best performance in DAGSVM. Furthermore, an analysis of features shows that the generalization performance of SVM can be further improved by performing feature selection.