Data-guided model combination by decomposition and aggregation

  title={Data-guided model combination by decomposition and aggregation},
  author={Mingyang Xu and Michael Golay},
  journal={Machine Learning},
Model selection and model combination is a general problem in many areas. Especially, when we have several different candidate models and also have gathered a new data set, we want to construct a more accurate and precise model in order to help predict future events. In this paper, we propose a new data-guided model combination method by decomposition and aggregation. With the aid of influence diagrams, we analyze the dependence among candidate models and apply latent factors to characterize… CONTINUE READING