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Learning Future Classifiers without Additional Data
追加データを用いない未来の分類器学習法

熊谷, 充敏 ; 岩田, 具治 ; Kumagai, Atsutoshi ; Iwata, Tomoharu

人工知能学会論文誌, 2018, Vol.33(2), pp.D-H92_1-9

ISSN: 1346-0714 ; DOI: 10.1527/tjsai.D-H92

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  • Learning Future Classifiers without Additional Data

  • Nhan đề:
    追加データを用いない未来の分類器学習法
  • Tác giả: 熊谷, 充敏 ; 岩田, 具治 ; Kumagai, Atsutoshi ; Iwata, Tomoharu
  • Chủ đề: machine learning ; classification ; concept drift ; time-series ; Machine Learning ; Classification ; Concept Drift ; Time-Series
  • Là 1 phần của: 人工知能学会論文誌, 2018, Vol.33(2), pp.D-H92_1-9
  • Mô tả:

    We propose probabilistic models for predicting future classifiers given labeled data with timestamps collected until the current time. In some applications, the decision boundary changes over time. For example, in activity recognition using sensor data, the decision boundary can vary since user activity patterns dynamically change. Existing methods require additional labeled and/or unlabeled data to learn a time-evolving decision boundary. However, collecting these data can be expensive or impossible. By incorporating time-series models to capture the dynamics of a decision boundary, the proposed model can predict future classifiers without additional data. We developed two learning algorithms for the proposed model on the basis of variational Bayesian inference. The effectiveness of the proposed method is demonstrated with experiments using synthetic and real-world data sets.


  • Ngôn ngữ: Japanese
  • Số nhận dạng: ISSN: 1346-0714 ; DOI: 10.1527/tjsai.D-H92

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