Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Generalization Error Bounds in Semi-supervised Classification Under the Cluster Assumption
The Journal of Machine Learning Research
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
On Efficient Large Margin Semisupervised Learning: Method and Theory
The Journal of Machine Learning Research
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Presence-only data occur in a classification, which consist of a sample of observations from the presence class and a large number of background observations with unknown presence/absence. Since absence data are generally unavailable, conventional semi-supervised learning approaches are no longer appropriate as they tend to degenerate and assign all observations to the presence class. In this article, we propose a generalized class balance constraint, which can be equipped with semi-supervised learning approaches to prevent them from degeneration. Furthermore, to circumvent the difficulty of model tuning with presence-only data, a selection criterion based on classification stability is developed, which measures the robustness of any given classification algorithm against the sampling randomness. The effectiveness of the proposed approach is demonstrated through a variety of simulated examples, along with an application to gene function prediction.