---
res:
bibo_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.@eng'
bibo_authorlist:
- foaf_Person:
foaf_givenName: Christoph
foaf_name: Lampert, Christoph
foaf_surname: Lampert
foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0001-8622-7887
bibo_doi: 10.1109/CVPR.2015.7298696
dct_date: 2015^xs_gYear
dct_language: eng
dct_publisher: IEEE@
dct_title: Predicting the future behavior of a time-varying probability distribution@
...