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Bayesian models for Large-scale Hierarchical Classification
2012
Neural Information Processing Systems
A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance. An even greater challenge is to do so in a manner that is computationally feasible for large scale problems. This paper proposes a set of Bayesian methods to model hierarchical dependencies among class labels using multivariate logistic regression. Specifically, the parent-child relationships are modeled by placing a hierarchical prior over the
dblp:conf/nips/GopalYBN12
fatcat:dhb2ovrk3vadjivyydjxsv5g4y