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Sampling and Learning of the And-Or Graph


The And-Or graph is a tool for knowledge representation. In this thesis we first study the

sampling of the And-Or graph with or without context constraints. Without any constraint

on the potential functions of the And-Or graph nodes, the positions and shapes of differ-

ent components of the face images are not aligned properly. In contrast, with both unary

constraints and binary constraints, the components are aligned and the samples are more

representative of the And-Or graph. We further explore parameter and structure learning of

the And-Or graph by implementing and applying some existing algorithms. The experimen-

tal results on 1D text data and 2D face image data are shown. While there is no apparent

difference between the sampling results of the parameter learned And-Or graph and the true

And-Or graph, the sampling results of the structure learned And-Or graph are not perfect

and could be further improved.

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