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Relational Learning, and Structured Output

Relational Learning, and Structured Output. Lei Tang May.04, 2007. Relational Learning. Typical Classification task: IID assumption Relational Learning: instances are interrelated. Some Examples: Hypertext Classification Identifying topics in scientific literature.

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Relational Learning, and Structured Output

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  1. Relational Learning, and Structured Output Lei Tang May.04, 2007

  2. Relational Learning • Typical Classification task: IID assumption • Relational Learning: instances are interrelated. • Some Examples: • Hypertext Classification • Identifying topics in scientific literature. • Prediction of Movie Box Sales

  3. Strategies for relational learning • How to use the link information? • Make it flat, several models (Jens-etal04, kdd)

  4. Ensemble Method • Consider relational learning as datasets with heterogonous attributes. • Another strategy is to build classifier on each type of attribute and do the ensemble learning.

  5. Iterative Strategy for Prediction • For prediction, the class labels are unknown for the neighbors of the test data. • So, iteratively predict until converge. (Nevi-Jens00, AAAI; Li-Geto03, ICML) • This like label propagation in semi-supervised learning. • Baseline models: • Use only attributes • Use only link information (This actually works pretty well on most benchmark datasets used in literature)

  6. Interdependent & Structured Output • The output space is not a “flat” list of labels, but some structure. • E.g. Multi-label/class classification, Hierarchical Classification

  7. Collective Multi-label Classification • Takes into consideration of interrelations between labels rather than instances.

  8. Large-Margin Methods • Use Joint Feature Map.

  9. Discussions • Interdependent features (graphical model) • Interdependent instances (not well studied yet) • Interdependent classes (The future)

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