Latent space domain transfer between high dimensional overlapping distributions
Proceedings of the 18th international conference on World wide web
Artificial neural network reduction through oracle learning
Intelligent Data Analysis
Semisupervised multicategory classification with imperfect model
IEEE Transactions on Neural Networks
Semi-supervised learning based on high density region estimation
Neural Networks
Semi-Supervised Novelty Detection
The Journal of Machine Learning Research
A cluster-assumption based batch mode active learning technique
Pattern Recognition Letters
Analysis of presence-only data via semi-supervised learning approaches
Computational Statistics & Data Analysis
Structural twin parametric-margin support vector machine for binary classification
Knowledge-Based Systems
Hi-index | 0.00 |
We consider semi-supervised classification when part of the available data is unlabeled. These unlabeled data can be useful for the classification problem when we make an assumption relating the behavior of the regression function to that of the marginal distribution. Seeger (2000) proposed the well-known cluster assumption as a reasonable one. We propose a mathematical formulation of this assumption and a method based on density level sets estimation that takes advantage of it to achieve fast rates of convergence both in the number of unlabeled examples and the number of labeled examples.