Estimating Non additive Value Functions With Active Learning In The Ordinal Classification Setting

Erişkin, Levent
Köksal, Gülser


Estimating Partially Observed Graph Signals by Learning Spectrally Concentrated Graph Kernels
Turhan, Gulce; Vural, Elif (2021-01-01)
© 2021 IEEE.Graph models provide flexible tools for the representation and analysis of signals defined over irregular domains such as social or sensor networks. However, in real applications data observations are often not available over the whole graph, due to practical problems such as sensor failure or connection loss. In this paper, we study the estimation of partially observed graph signals on multiple graphs. We learn a sparse representation of partially observed graph signals over spectrally concentr...
Estimating polynomial structures from radar data
Lundquist, Christian; Orguner, Umut; Gustafsson, Fredrik (IEEE Computer Society; 2010-07-29)
Situation awareness for vehicular safety and autonomy functions includes knowledge of the drivable area. This area is normally constrained between stationary road-side objects as guard-rails, curbs, ditches and vegetation. We consider these as extended objects modeled by polynomials along the road, and propose an algorithm to track each polynomial based on noisy range and bearing detections, typically from a radar. A straightforward Kalman filter formulation of the problem suffers from the errors-in-variabl...
Estimating variance components
Yuva, Filiz; Güven, Bilgehan; Gönen, Soner; Department of Statistics (2002)
Estimating parameters of a multiple autoregressive model by the modified maximum likelihood method
TÜRKER BAYRAK, ÖZLEM; Akkaya, Ayşen (Elsevier BV, 2010-02-15)
We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have convergence problems (Puthenpura and Sinha, 1986) [11], we work Out modified maximum likelihood equations by expressing the maximum likelihood equations in terms of ordered residuals and linearizing intractable nonlinear functions (Tiku and Suresh, 1992) [8]. The solutions, called modified maximu...
Estimating Box-Cox power transformation parameter via goodness-of-fit tests
Asar, Ozgur; İlk Dağ, Özlem; DAĞ, OSMAN (Informa UK Limited, 2017-01-01)
Box-Cox power transformation is a commonly used methodology to transform the distribution of the data into a normal distribution. The methodology relies on a single transformation parameter. In this study, we focus on the estimation of this parameter. For this purpose, we employ seven popular goodness-of-fit tests for normality, namely Shapiro-Wilk, Anderson-Darling, Cramer-von Mises, Pearson Chi-square, Shapiro-Francia, Lilliefors and Jarque-Bera tests, together with a searching algorithm. The searching al...
Citation Formats
L. Erişkin and G. Köksal, “Estimating Non additive Value Functions With Active Learning In The Ordinal Classification Setting,” 2015, Accessed: 00, 2021. [Online]. Available: