A Dependence Stability Bound based on the VC Dimension for Relational Classification

Xing Wang, Hui He, Bin-Xing Fang, Hong-Li Zhang
2015 International Journal of Database Theory and Application  
Relational classification (RC) is concerned with the application of statistical learning to relational data. RC models do not have improved stability to smooth the perturbations generated by variations in the correlation between the relational data. Therefore, few studies have attempted to derive a bound and develop a stability learning framework for RC models. To solve this problem, we derive a learning bound with a new measure dependence stability and a limited Vapnik-Chervonenkis (VC)
more » ... on. Based on the learning bound, we then design a stable learning framework that serves as a guideline for the development of new learning algorithms for a broad class of RC models. Applying a Markov logic network on synthesized and real-world datasets, our experimental results demonstrate that our bound can be tight if the RC model has appropriate dependence stability and limited VC dimension and our learning framework increases the stability of RC models while reducing the deviation between empirical risk and true risk.
doi:10.14257/ijdta.2015.8.3.11 fatcat:tjonthkl7vdrbgknavwng5hkv4