The nature of statistical learning theory
The nature of statistical learning theory
From data distributions to regularization in invariant learning
Neural Computation
Geometry and invariance in kernel based methods
Advances in kernel methods
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
The Journal of Machine Learning Research
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
Rough cluster algorithm based on kernel function
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Support Vector Machine incorporated with feature discrimination
Expert Systems with Applications: An International Journal
Rough kernel clustering algorithm with adaptive parameters
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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This paper presents a general method for incorporating prior knowledge into kernel methods such as support vector machines. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assuming that all points in the given object share the same desired class. A number of implementation techniques of this method, based on hard geometrical objects and soft objects based on distributions are considered. Tangent vectors are extensively used for object construction. Empirical results on one artificial dataset and two real datasets of electro-encephalogram signals and face images demonstrate the usefulness of the proposed method. The method could establish a foundation for an information retrieval and person identification systems.