Predicting the future behavior of a time-varying probability distribution Conference Paper

Author(s): Lampert, Christoph
Title: Predicting the future behavior of a time-varying probability distribution
Affiliation IST Austria
Abstract: We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an interesting academic problem, but solving this extrapolation problem also has many practical application, e.g. for training classifiers that have to operate under time-varying conditions. Our main contribution is a method for predicting the next step of the time-varying distribution from a given sequence of sample sets from earlier time steps. For this we rely on two recent machine learning techniques: embedding probability distributions into a reproducing kernel Hilbert space, and learning operators by vector-valued regression. We illustrate the working principles and the practical usefulness of our method by experiments on synthetic and real data. We also highlight an exemplary application: training a classifier in a domain adaptation setting without having access to examples from the test time distribution at training time.
Conference Title: CVPR: Computer Vision and Pattern Recognition
Conference Dates: June 7 - June 12, 2015
Conference Location: Boston, MA, USA
Publisher: IEEE  
Date Published: 2015-06-01
Start Page: 942
End Page: 950
DOI: 10.1109/CVPR.2015.7298696
Open access: yes (repository)
IST Austria Authors
  1. Christoph Lampert
    87 Lampert
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