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| − | A decision tree is a classifier that maps from the observation about an item to the conclusion about its target value. It is also realized to be deterministic ancestor of the hierarchical mixture of experts. Decison tree use can be traced back from problems that require probabilistic inference which is a core technology in AI, largely due to developments in graph-theoretic methods for the representation and manipulation of complex probability distributions. Whether in their guise as directed graphs (Bayesian networks) or as undirected graphs (Markov random fields), probabilistic decision trees/models have a number of virtues as representations of uncertainty and as inference engines. The foundation formed by decision trees forms the basis for Graphican Models. | + | A decision tree is a classifier that maps from the observation about an item to the conclusion about its target value. It is also realized to be deterministic ancestor of the hierarchical mixture of experts. Decison tree use can be traced back from problems that require probabilistic inference which is a core technology in AI, largely due to developments in graph-theoretic methods for the representation and manipulation of complex probability distributions. Whether in their guise as directed graphs (Bayesian networks) or as undirected graphs (Markov random fields), probabilistic decision trees/models have a number of virtues as representations of uncertainty and as inference engines. The foundation formed by decision trees forms the basis for Graphican Models.(See [[Graphical Models_OldKiwi]]). |
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| + | Also see [[Influence diagram_OldKiwi]] | ||
Latest revision as of 21:42, 20 April 2008
A decision tree is a classifier that maps from the observation about an item to the conclusion about its target value. It is also realized to be deterministic ancestor of the hierarchical mixture of experts. Decison tree use can be traced back from problems that require probabilistic inference which is a core technology in AI, largely due to developments in graph-theoretic methods for the representation and manipulation of complex probability distributions. Whether in their guise as directed graphs (Bayesian networks) or as undirected graphs (Markov random fields), probabilistic decision trees/models have a number of virtues as representations of uncertainty and as inference engines. The foundation formed by decision trees forms the basis for Graphican Models.(See Graphical Models_OldKiwi).
Also see Influence diagram_OldKiwi
