Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Computation
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Neural Computation
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Mutually beneficial learning with application to on-line news classification
Proceedings of the ACM first Ph.D. workshop in CIKM
Using domain-specific knowledge in generalization error bounds for support vector machine learning
Decision Support Systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Incorporating a priori knowledge from detractor points into support vector classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A theoretical framework for supervised learning from regions
Neurocomputing
Hi-index | 0.00 |
If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we introduce a simple method to incorporate prior knowledge in support vector machines by modifying the hypothesis space rather than the optimization problem. The optimization problem is amenable to solution by the constrained concave convex procedure, which finds a local optimum. The paper discusses different kinds of prior knowledge and demonstrates the applicability of the approach in some characteristic experiments.