@article {14403,
	title = {Tutorial on statistical relational learning},
	journal = {Inductive Logic Programming},
	year = {2005},
	month = {2005///},
	pages = {415 - 415},
	abstract = {Statistical machine learning is in the midst of a {\textquotedblleft}relational revolution{\textquotedblright}. After many decades of focusing on independent and identically-distributed (iid) examples, many researchers are now studying problems in which the examples are linked together into complex networks. These networks ca be a simple as sequences and 2-D meshes (such as those arising in part-of-speech tagging and remote sensing) or as complex as citation graphs, the world wide web, and relational data bases.Statistical relational learning raises many new challenges and opportunities. Because the statistical model depends on the domain{\textquoteright}s relational structure, parameters in the model are often tied. This has advantages for making parameter estimation feasible, but complicates the model search. Because the {\textquotedblleft}features{\textquotedblright} involve relationships among multiple objects, there is often a need to intelligently construct aggregates and other relational features. Problems that arise from linkage and autocorrelation among objects must be taken into account. Because instances are linked together, classification typically involves complex inference to arrive at {\textquotedblleft}collective classification{\textquotedblright} in which the labels predicted for the test instances are determined jointly rather than individually. Unlike iid problems, where the result of learning is a single classifier, relational learning often involves instances that are heterogeneous, where the result of learning is a set of multiple components (classifiers, probability distributions, etc.) that predict labels of objects and logical relationships between objects.
},
	doi = {10.1007/11536314_26},
	author = {Getoor, Lise}
}
