Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
New branch-and-bound rules for linear bilevel programming
SIAM Journal on Scientific and Statistical Computing
The nature of statistical learning theory
The nature of statistical learning theory
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Restricted Bayes Optimal Classifiers
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Eighteenth national conference on Artificial intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
A model of inductive bias learning
Journal of Artificial Intelligence Research
Rotational prior knowledge for SVMs
ECML'05 Proceedings of the 16th European conference on Machine Learning
A hidden Markov model-based text classification of medical documents
Journal of Information Science
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We present a novel framework for integrating prior knowledge into discriminative classifiers. Our framework allows discriminative classifiers such as Support Vector Machines (SVMs) to utilize prior knowledge specified in the generative setting. The dual objective of fitting the data and respecting prior knowledge is formulated as a bilevel program, which is solved (approximately) via iterative application of second-order cone programming. To test our approach, we consider the problem of using WordNet (a semantic database of English language) to improve low-sample classification accuracy of newsgroup categorization. WordNet is viewed as an approximate, but readily available source of background knowledge, and our framework is capable of utilizing it in a flexible way.