Mixtures of Bagged Markov Tree Ensembles
نویسندگان
چکیده
Key points: •Trees → efficient algorithms. •Mixture → improved modeling. There are 2 approaches to improve over a single Chow-Liu tree: Bias reduction, e.g. EM algorithm [1] •Learning the mixture is viewed as a global optimization problem aiming at maximizing the data likelihood. •There is a bias-variance trade-off associated with the number of terms. • It leads to a partition of the learning set: each tree models a subset of observations.
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تاریخ انتشار 2012