The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Nature Of Statistical Learning Theory~
IEEE Transactions on Neural Networks
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels
IEEE Transactions on Neural Networks
Virtual cinematography director for interactive storytelling
Proceedings of the International Conference on Advances in Computer Enterntainment Technology
A data-driven approach to manage the length of stay for appendectomy patients
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Performance evaluation of score level fusion in multimodal biometric systems
Pattern Recognition
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Statistical learning theory started more than 30 years ago. Until the middle of the 1990's, the success of support vector machine (SVM) in solving real-life problems made it not only a tool for the theoretical analysis but also a tool for creating practical algorithms for real-world problems. In this paper, we present a general overview of statistical learning theory andtheoretically analyze the reason of overfitting problem in statistical learning. We also describe the current state of the art in SVM. Finally, as an application of SVM, we present experimental results in our implementation of SVM and demonstrate its advantage in multiuser detection problem.