The nature of statistical learning theory
The nature of statistical learning theory
Lower bound on VC-dimension by local shattering
Neural Computation
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Does extra knowledge necessarily improve generalization?
Neural Computation
Analysis of perceptron-based active learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
A simple and effective method for incorporating advice into kernel methods
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Generative prior knowledge for discriminative classification
Journal of Artificial Intelligence Research
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Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear support vector machines in form of constraints on the rotation of the normal to the separating hyperplane. Such knowledge frequently arises naturally, e.g., as inhibitory and excitatory influences of input variables. We demonstrate that the generalization ability of rotationally-constrained classifiers is improved by analyzing their VC and fat-shattering dimensions. Interestingly, the analysis shows that large-margin classification framework justifies the use of stronger prior knowledge than the traditional VC framework. Empirical experiments with text categorization and political party affiliation prediction confirm the usefulness of rotational prior knowledge.