An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
An Error Bound Based on a Worst Likely Assignment
The Journal of Machine Learning Research
Nearly Uniform Validation Improves Compression-Based Error Bounds
The Journal of Machine Learning Research
Graph Theory
Proceedings of the 18th international conference on World wide web
Statistical Analysis of Network Data: Methods and Models
Statistical Analysis of Network Data: Methods and Models
Collective classification using heterogeneous classifiers
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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This paper develops PAC (probably approximately correct) error bounds for network classifiers in the transductive setting, where the network node inputs and links are all known, the training nodes class labels are known, and the goal is to classify a working set of nodes that have unknown class labels. The bounds are valid for any model of network generation. They require working nodes to be selected independently, but not uniformly at random. For example, they allow different regions of the network to have different densities of unlabeled nodes.