论文信息 - An Investigation into Mini-Batch Rule Learning

An Investigation into Mini-Batch Rule Learning

We investigate whether it is possible to learn rule sets efficiently in a network structure with a single hidden layer using iterative refinements over mini-batches of examples. A first rudimentary version shows an acceptable performance on all but one dataset, even though it does not yet reach the performance levels of Ripper.

Johannes Fürnkranz | Florian Beck

[1] Johannes Fürnkranz,et al. Foundations of Rule Learning , 2012, Cognitive Technologies.

[2] Eyke Hüllermeier,et al. Learning Gradient Boosted Multi-label Classification Rules , 2020, ArXiv.

[3] Luc De Raedt,et al. Phase Transitions and Stochastic Local Search in k-Term DNF Learning , 2002, ECML.

[4] Fan Yang,et al. Differentiable Learning of Logical Rules for Knowledge Base Reasoning , 2017, NIPS.

[5] João Gama,et al. Very fast decision rules for classification in data streams , 2013, Data Mining and Knowledge Discovery.

[6] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.

[7] Wojciech Kotlowski,et al. ENDER: a statistical framework for boosting decision rules , 2010, Data Mining and Knowledge Discovery.

[8] Fan Yang,et al. Differentiable Learning of Logical Rules for Knowledge Base Completion , 2017, ArXiv.