### Abstract

The hierarchical mixture of experts (HME) is a tree-structured probabilistic model for regression and classification. The HME has a considerable expression capability, however, the estimation of the parameters tends to overfit due to the complexity of the model. To avoid this problem, regularization techniques are widely used. In particular, it is known that a sparse solution can be obtained by L1 regularization. From a Bayesian point of view, regularization techniques are equivalent to assume that the parameters follow prior distributions and find the maximum a posteriori probability estimator. It is known that L1 regularization is equivalent to assuming Laplace distributions as prior distributions. However, it is difficult to compute the posterior distribution if Laplace distributions are assumed. In this paper, we assume that the parameters of the HME follow hierarchical prior distributions which are equivalent to Laplace distribution to promote sparse solutions. We propose a Bayesian estimation algorithm based on the variational method. Finally, the proposed algorithm is evaluated by computer simulations.

Original language | English |
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Title of host publication | Proceedings of 2018 International Symposium on Information Theory and Its Applications, ISITA 2018 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 60-64 |

Number of pages | 5 |

ISBN (Electronic) | 9784885523182 |

DOIs | |

Publication status | Published - 2019 Mar 8 |

Event | 15th International Symposium on Information Theory and Its Applications, ISITA 2018 - Singapore, Singapore Duration: 2018 Oct 28 → 2018 Oct 31 |

### Publication series

Name | Proceedings of 2018 International Symposium on Information Theory and Its Applications, ISITA 2018 |
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### Conference

Conference | 15th International Symposium on Information Theory and Its Applications, ISITA 2018 |
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Country | Singapore |

City | Singapore |

Period | 18/10/28 → 18/10/31 |

### ASJC Scopus subject areas

- Computer Science Applications
- Information Systems

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## Cite this

*Proceedings of 2018 International Symposium on Information Theory and Its Applications, ISITA 2018*(pp. 60-64). [8664333] (Proceedings of 2018 International Symposium on Information Theory and Its Applications, ISITA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ISITA.2018.8664333