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  Probabilistic Progress Bars

Kiefel, M., Schuler, C. J., & Hennig, P. (2014). Probabilistic Progress Bars. In J. Xiaoyi, J. Hornegger, & R. Koch (Eds.), Pattern Recognition. 36th German Conference, GCPR 2014. Proceedings (pp. 331-342). Cham et al.: Springer International Publishing AG.

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 Creators:
Kiefel, Martin1, Author              
Schuler, Christian J.1, Author              
Hennig, Philipp1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf; Abt. Black
 Abstract: Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.

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Language(s):
 Dates: 2014-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: kiefel14gcpr
 Degree: -

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Title: GCPR 2014. 36th German Conference on Pattern Recognition
Place of Event: Münster
Start-/End Date: 2014-09-02 - 2014-09-05

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Title: Pattern Recognition. 36th German Conference, GCPR 2014. Proceedings
Source Genre: Proceedings
 Creator(s):
Xiaoyi, Jiang, Editor
Hornegger, Joachim, Editor
Koch, Reinhard, Editor
Affiliations:
-
Publ. Info: Cham et al. : Springer International Publishing AG
Pages: 775 Volume / Issue: - Sequence Number: - Start / End Page: 331 - 342 Identifier: ISBN: 978-3-319-11752-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Affiliations:
Publ. Info: Springer
Pages: - Volume / Issue: 8753 Sequence Number: - Start / End Page: - Identifier: -