Blar i Fakultet for teknologi, kunst og design (TKD) på emneord "Data streams"

Viser treff 1-6 av 6

    • Estimating tukey depth using incremental quantile estimators 

      Hammer, Hugo Lewi; Yazidi, Anis; Rue, Håvard (Pattern Recognition;Volume 122, February 2022, 108339, Peer reviewed; Journal article, 2022)
      Measures of distance or how data points are positioned relative to each other are fundamental in pattern recognition. The concept of depth measures how deep an arbitrary point is positioned in a dataset, and is an interesting ...

    • Joint tracking of multiple quantiles through conditional quantiles 

      Hammer, Hugo Lewi; Yazidi, Anis; Rue, Håvard (Information Sciences;Volume 563, July 2021, Peer reviewed; Journal article, 2021-03-05)
      The estimation of quantiles is one of the most fundamental data mining tasks. As most real-time data streams vary dynamically over time, there is a quest for adaptive quantile estimators. The most well-known type of adaptive ...

    • A new quantile tracking algorithm using a generalized exponentially weighted average of observations 

      Hammer, Hugo Lewi; Yazidi, Anis; Rue, Håvard (Applied Intelligence;Published online 10 November, 2018, Journal article; Journal article; Peer reviewed, 2018-11-10)
      The Exponentially Weighted Average (EWA) of observations is known to be state-of-art estimator for tracking expectations of dynamically varying data stream distributions. However, how to devise an EWA estimator to rather ...

    • On the Classification of Dynamical Data Streams Using Novel “Anti–Bayesian” Techniques 

      Hammer, Hugo Lewi; Yazidi, Anis; Oommen, John (Journal article; Peer reviewed, 2018)
      The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, ...

    • On utilizing weak estimators to achieve the online classification of data streams 

      Tavasoli, Hanane; Oommen, John; Yazidi, Anis (Engineering Applications of Artificial Intelligence;Volume 86, November 2019, Journal article; Peer reviewed, 2019-09-02)
      Classification, typically, deals with unique and distinct training and testing phases. This paper pioneers the concept when these phases are not so clearly well-defined. More specifically, we consider the case where the ...

    • Parameter estimation in abruptly changing dynamic environments using stochastic learning weak estimator 

      Hammer, Hugo Lewi; Yazidi, Anis (Applied Intelligence;Volume 48, Issue 11, Journal article; Peer reviewed, 2018-05-25)
      Many real-life dynamical systems experience abrupt changes followed by almost stationary periods. In this paper, we consider streams of data exhibiting such abrupt behavior and investigate the problem of tracking their ...