An efficient algorithm for learning to rank from preference graphs
Machine Learning
Error bounds of multi-graph regularized semi-supervised classification
Information Sciences: an International Journal
Transductive Rademacher complexity and its applications
Journal of Artificial Intelligence Research
Semisupervised multicategory classification with imperfect model
IEEE Transactions on Neural Networks
Semi-supervised learning based on high density region estimation
Neural Networks
Towards open-domain Semantic Role Labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Semi-supervised Bayesian ARTMAP
Applied Intelligence
Neurocomputing
An effective procedure exploiting unlabeled data to build monitoring system
Expert Systems with Applications: An International Journal
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Proceedings of the 5th ACM workshop on Security and artificial intelligence
Information Sciences: an International Journal
Kernel regression with sparse metric learning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 754.84 |
In this paper, we consider a framework for semi-supervised learning using spectral decomposition-based unsupervised kernel design. We relate this approach to previously proposed semi-supervised learning methods on graphs. We examine various theoretical properties of such methods. In particular, we present learning bounds and derive optimal kernel representation by minimizing the bound. Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can improve the predictive performance. Empirical examples are included to illustrate the main consequences of our analysis.