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
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Face Recognition
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Semi-Supervised Learning
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
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Classification is one of the most fundamental problems in machine learning, which aims to separate the data from different classes as far away as possible. A common way to get a good classification function is to minimize its empirical prediction loss or structural loss. In this paper, we point out that we can also enhance the discriminality of those classifiers by further incorporating the discriminative information contained in the data set as a prior into the classifier construction process. In such a way, we will show that the constructed classifiers will be more powerful, and this will also be validated by the final empirical study on several benchmark data sets.