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A Statistical Learning Theory Framework for Supervised Pattern Discovery
[chapter]
2014
Proceedings of the 2014 SIAM International Conference on Data Mining
This paper formalizes a latent variable inference problem we call supervised pattern discovery, the goal of which is to find sets of observations that belong to a single "pattern." We discuss two versions of the problem and prove uniform risk bounds for both. In the first version, collections of patterns can be generated in an arbitrary manner and the data consist of multiple labeled collections. In the second version, the patterns are assumed to be generated independently by identically
doi:10.1137/1.9781611973440.58
dblp:conf/sdm/HugginsR14
fatcat:ok5bn3jrgjhfrdy6cn6qnxeoci