Introduction Relational Learning o Traditional machine learning models: Assumption:i.i.d. 。Advantage:simple Many real-world applications: Relational:instances are related (linked)to each other Autocorrelation:statistical dependency between the values of a random variable on related objects(non i.i.d.) .E.g.,web pages,protein-protein interaction data o Relational learning: An emerging research area attempting to represent,reason,and learn in domains with complex relational structure [Getoor Taskar,2007] ●Application areas: Web mining.social network analysis,bioinformatics,marketing,etc. 三)Q0 Li,Zhang and Yeung (CSE.HKUST) LWP A1 STATS20093/23Introduction Relational Learning Traditional machine learning models: Assumption: i.i.d. Advantage: simple Many real-world applications: Relational: instances are related (linked) to each other Autocorrelation: statistical dependency between the values of a random variable on related objects (non i.i.d.) E.g., web pages, protein-protein interaction data Relational learning: An emerging research area attempting to represent, reason, and learn in domains with complex relational structure [Getoor & Taskar, 2007]. Application areas: Web mining, social network analysis, bioinformatics, marketing, etc. Li, Zhang and Yeung (CSE, HKUST) LWP AISTATS 2009 3 / 23