### Abstract

This paper addresses a problem in which we learn a regression model from sets of training data. Each of the sets has an only single label, and only one of the training data in the set reflects the label. This is particularly the case when the label is attached to a group of data, such as time-series data. The label is not attached to the point of the sequence but rather attached to particular time window of the sequence. As such, a small part of the time window likely reflects the label, whereas the other larger part of the time window likely does not reflect it. We design an algorithm for estimating which of the training data in each of the sets corresponds to the label, as well as for training the regression model on the basis of Bayesian modeling and posterior inference with variational Bayes. Our experimental results show that our approach perform better than baseline methods on an artificial dataset and on a real-world dataset.

Original language | English |
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Title of host publication | 2016 23rd International Conference on Pattern Recognition, ICPR 2016 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 2580-2585 |

Number of pages | 6 |

ISBN (Electronic) | 9781509048472 |

DOIs | |

Publication status | Published - 2017 Apr 13 |

Event | 23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: 2016 Dec 4 → 2016 Dec 8 |

### Other

Other | 23rd International Conference on Pattern Recognition, ICPR 2016 |
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Country | Mexico |

City | Cancun |

Period | 16/12/4 → 16/12/8 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Vision and Pattern Recognition

### Cite this

*2016 23rd International Conference on Pattern Recognition, ICPR 2016*(pp. 2580-2585). [7900024] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7900024

**Bayesian regression selecting valuable subset from mixed bag training data.** / Katsuki, Takayuki; Inoue, Masato.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2016 23rd International Conference on Pattern Recognition, ICPR 2016.*, 7900024, Institute of Electrical and Electronics Engineers Inc., pp. 2580-2585, 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 16/12/4. https://doi.org/10.1109/ICPR.2016.7900024

}

TY - GEN

T1 - Bayesian regression selecting valuable subset from mixed bag training data

AU - Katsuki, Takayuki

AU - Inoue, Masato

PY - 2017/4/13

Y1 - 2017/4/13

N2 - This paper addresses a problem in which we learn a regression model from sets of training data. Each of the sets has an only single label, and only one of the training data in the set reflects the label. This is particularly the case when the label is attached to a group of data, such as time-series data. The label is not attached to the point of the sequence but rather attached to particular time window of the sequence. As such, a small part of the time window likely reflects the label, whereas the other larger part of the time window likely does not reflect it. We design an algorithm for estimating which of the training data in each of the sets corresponds to the label, as well as for training the regression model on the basis of Bayesian modeling and posterior inference with variational Bayes. Our experimental results show that our approach perform better than baseline methods on an artificial dataset and on a real-world dataset.

AB - This paper addresses a problem in which we learn a regression model from sets of training data. Each of the sets has an only single label, and only one of the training data in the set reflects the label. This is particularly the case when the label is attached to a group of data, such as time-series data. The label is not attached to the point of the sequence but rather attached to particular time window of the sequence. As such, a small part of the time window likely reflects the label, whereas the other larger part of the time window likely does not reflect it. We design an algorithm for estimating which of the training data in each of the sets corresponds to the label, as well as for training the regression model on the basis of Bayesian modeling and posterior inference with variational Bayes. Our experimental results show that our approach perform better than baseline methods on an artificial dataset and on a real-world dataset.

UR - http://www.scopus.com/inward/record.url?scp=85019089447&partnerID=8YFLogxK

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U2 - 10.1109/ICPR.2016.7900024

DO - 10.1109/ICPR.2016.7900024

M3 - Conference contribution

SP - 2580

EP - 2585

BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -