Learning from networked examples in a k-partite graph
This addresses the challenge of handling non-independent data in machine learning, but it appears incremental as it builds on prior work with specific improvements.
The paper tackles the problem of learning from networked examples where training examples are not independent, proposing an efficient weighting method and achieving a better sample error bound than previous work.
Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample where two or more training examples may share common features. We propose an efficient weighting method for learning from networked examples and show the sample error bound which is better than previous work.