Probabilistic abstraction hierarchies Export

@misc{citeulike:620756, abstract = {Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in \"nearby\" classes in the taxonomy are similar. In this paper, we provide a general probabilistic framework for clustering data into a set of classes organized as a taxonomy, where each class is associated with a probabilistic model from which the data was generated. The clustering algorithm simultaneously optimizes three things: the assignment of data instances to clusters, the models...}, author = {Segal, E. and Koller, D. and Ormoneit, D.}, citeulike-article-id = {620756}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8906}, keywords = {hierarchy, probabilistic-models}, posted-at = {2006-05-09 22:46:28}, priority = {3}, title = {Probabilistic abstraction hierarchies}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8906}, year = {2001} }

TY - GEN ID - citeulike:620756 L3 - citeulike-article-id:620756 N2 - Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in "nearby" classes in the taxonomy are similar. In this paper, we provide a general probabilistic framework for clustering data into a set of classes organized as a taxonomy, where each class is associated with a probabilistic model from which the data was generated. The clustering algorithm simultaneously optimizes three things: the assignment of data instances to clusters, the models... TI - Probabilistic abstraction hierarchies KW - hierarchy KW - probabilistic-models AU - Segal, E AU - Koller, D AU - Ormoneit, D PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8906 ER -