Browsing by Subject "Finite state machine"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    Sequential prediction over hierarchical structures
    (Institute of Electrical and Electronics Engineers Inc., 2016) Vanli, N. D.; Gokcesu, K.; Sayin, M. O.; Yildiz, H.; Kozat, S. S.
    We study sequential compound decision problems in the context of sequential prediction of real valued sequences. In particular, we consider finite state (FS) predictors that are constructed based on a hierarchical structure, such as the order preserving patterns of the sequence history. We define hierarchical equivalence classes by tying certain models at a hierarchy level in a recursive manner in order to mitigate undertraining problems. These equivalence classes defined on a hierarchical structure are then used to construct a super exponential number of sequential FS predictors based on their combinations and permutations. We then introduce truly sequential algorithms with computational complexity only linear in the pattern length that 1) asymptotically achieve the performance of the best FS predictor or the best linear combination of all the FS predictors in an individual sequence manner without any stochastic assumptions over any data length n under a wide range of loss functions; 2) achieve the mean square error of the best linear combination of all FS filters or predictors in the steady-state for certain nonstationary models. We illustrate the superior convergence and tracking capabilities of our algorithm with respect to several state-of-the-art methods in the literature through simulations over synthetic and real benchmark data. © 1991-2012 IEEE.