Bayesian Sets Export

@misc{Ghahramani2005, abstract = {Inspired by \"Google Sets\", we consider the problem of retrieving items from a concept or cluster, given a query consisting of a few items from that cluster. We formulate this as a Bayesian inference problem and describe a very simple algorithm for solving it. Our algorithm uses a modelbased concept of a cluster and ranks items using a score which evaluates the marginal probability that each item belongs to a cluster containing the query items. For exponential family models with...}, author = {Ghahramani, Z. and Heller, K. A.}, citeulike-article-id = {976721}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.1622}, keywords = {bayesian, graphical-model, learning, statistical-model}, posted-at = {2007-04-24 08:51:37}, priority = {2}, title = {Bayesian Sets}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.1622}, year = {2005} }

TY - GEN ID - Ghahramani2005 L3 - citeulike-article-id:976721 N2 - Inspired by "Google Sets", we consider the problem of retrieving items from a concept or cluster, given a query consisting of a few items from that cluster. We formulate this as a Bayesian inference problem and describe a very simple algorithm for solving it. Our algorithm uses a modelbased concept of a cluster and ranks items using a score which evaluates the marginal probability that each item belongs to a cluster containing the query items. For exponential family models with... TI - Bayesian Sets KW - bayesian KW - graphical-model KW - learning KW - statistical-model AU - Ghahramani, Z AU - Heller, KA PY - 2005/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.1622 ER -