The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
The Journal of Machine Learning Research
A general regression technique for learning transductions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Online option price forecasting by using unscented Kalman filters and support vector machines
Expert Systems with Applications: An International Journal
Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Chaos-based support vector regressions for exchange rate forecasting
Expert Systems with Applications: An International Journal
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
A logical analysis of banks' financial strength ratings
Expert Systems with Applications: An International Journal
Save the best for last? The treatment of dominant predictors in financial forecasting
Expert Systems with Applications: An International Journal
Credit rating using a hybrid voting ensemble
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
An information theoretic sparse kernel algorithm for online learning
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
The subprime mortgage crisis have triggered a significant economic decline over the world. Credit rating forecasting has been a critical issue in the global banking systems. The study trained a Gaussian process based multi-class classifier (GPC), a highly flexible probabilistic kernel machine, using variational Bayesian methods. GPC provides full predictive distributions and model selection simultaneously. During training process, the input features are automatically weighted by their relevances with respect to the output labels. Benefiting from the inherent feature scaling scheme, GPCs outperformed convectional multi-class classifiers and support vector machines (SVMs). In the second stage, conventional SVMs enhanced by feature selection and dimensionality reduction schemes were also compared with GPCs. Empirical results indicated that GPCs still performed the best.