论文信息 - A statistical approach to decision tree modeling

A statistical approach to decision tree modeling

A statistical approach to decision tree modeling is described. In this approach, each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions. The resulting model yields a likelihood measure of goodness of fit, allowing ML and MAP estimation techniques to be utilized. An efficient algorithm is presented to estimate the parameters in the tree. The model selection problem is presented and several alternative proposals are considered. A hidden Markov version of the tree is described for data sequences that have temporal dependencies.

Michael I. Jordan

[1] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[2] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[3] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[4] Leo Breiman,et al. Classification and Regression Trees , 1984 .

[5] Ronald L. Rivest,et al. Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[6] Carla E. Brodley,et al. An Incremental Method for Finding Multivariate Splits for Decision Trees , 1990, ML.

[7] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .

[8] Yoshua Bengio,et al. Credit Assignment through Time: Alternatives to Backpropagation , 1993, NIPS.

[9] Radford M. Neal. A new view of the EM algorithm that justifies incremental and other variants , 1993 .

[10] Michael I. Jordan,et al. Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[11] Steven J. Nowlan,et al. Mixtures of Controllers for Jump Linear and Non-Linear Plants , 1993, NIPS.

[12] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[13] Michael I. Jordan,et al. Convergence results for the EM approach to mixtures of experts architectures , 1995, Neural Networks.