Learning Bayesian Network Structure from Massive Datasets: The ”Sparse Candidate” Algorithm Export

@proceedings{citeulike:100302, abstract = {Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend most of the time examining candidates that are extremely unreasonable. This problem becomes critical when we deal with data sets that are...}, author = {Friedman, Nir and Nachman, Iftach and Pe'er, Dana}, citeulike-article-id = {100302}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.5605}, keywords = {algorithm, computer, programming, science}, pages = {206--215}, posted-at = {2006-01-15 00:22:08}, priority = {2}, title = {Learning Bayesian Network Structure from Massive Datasets: The ''Sparse Candidate'' Algorithm}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.5605} }

TY - CONF ID - citeulike:100302 L3 - citeulike-article-id:100302 N2 - Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend most of the time examining candidates that are extremely unreasonable. This problem becomes critical when we deal with data sets that are... EP - 215 TI - Learning Bayesian Network Structure from Massive Datasets: The ''Sparse Candidate'' Algorithm SP - 206 KW - algorithm KW - computer KW - programming KW - science AU - Friedman, Nir AU - Nachman, Iftach AU - Pe'er, Dana UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.5605 ER -