Now showing items 1-4 of 4

    • Competitive and online piecewise linear classification 

      Özkan, Hüseyin; Donmez, M.A.; Pelvan O.S.; Akman, A.; Kozat, Süleyman S. (IEEE, 2013)
      In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a ...
    • Efficient NP tests for anomaly detection over birth-death type DTMCs 

      Ozkan, H.; Ozkan, F.; Delibalta, I.; Kozat, S. S. (Springer New York LLC, 2018)
      We propose computationally highly efficient Neyman-Pearson (NP) tests for anomaly detection over birth-death type discrete time Markov chains. Instead of relying on extensive Monte Carlo simulations (as in the case of the ...
    • Novelty detection using soft partitioning and hierarchical models 

      Ergen, Tolga; Gökçesu, Kaan; Şimşek, Mustafa; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we study novelty detection problem and introduce an online algorithm. The algorithm sequentially receives an observation, generates a decision and then updates its parameters. In the first step, to model the ...
    • Online anomaly detection under Markov statistics with controllable type-I error 

      Ozkan, H.; Ozkan, F.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2016)
      We study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a ...