Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expert Systems with Applications: An International Journal
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Support vector machines (SVM) have demonstrated excellent performance in numerous areas of pattern recognitions. However, traditional SVM does not make efficient use of both labeled training data and unlabeled testing data. Moreover, high dimensional and nonlinear distributed data generally degrade the performance of a classifier due to the curse of dimensionality in financial distress (or bankruptcy) predictions. To address these problems, this study proposes a novel hybrid classifier which integrates Kernel local Fisher discriminant analysis (KLFDA) with a manifold-regularized SVM (MR-SVM). KLFDA is employed to find an optimal projection which maximizes the margin between data points from different classes at each local area of data manifold, while MR-SVM data-dependently warps the structure of feature space to reflect the underlying geometry of the data manifold. Compared with other dimensionality reduction methods and conventional classifiers, the hybrid classifier performs best.