3 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 3 / 3
Yayın Mixture of Gaussian models and bayes error under differential privacy(2011) Xi, Bowei; Kantarcıoğlu, Murat; İnan, AliGaussian mixture models are an important tool in Bayesian decision theory. In this study, we focus on building such models over statistical database protected under differential privacy. Our approach involves querying necessary statistics from a database and building a Bayesian classifier over the noise added responses generated according to differential privacy. We formally analyze the sensitivity of our query set. Since there are multiple methods to query a statistic, either directly or indirectly, we analyze the sensitivities for different querying methods. Furthermore we establish theoretical bounds for the Bayes error for the univariate (one dimensional) case. We study the Bayes error for the multivariate (high dimensional) case in experiments with both simulated data and real life data. We discover that adding Laplace noise to a statistic under certain constraint is problematic. For example variance-covariance matrix is no longer positive definite after noise addition. We propose a heuristic method to fix the noise added variance-covariance matrix.Yayın Driver recognition using gaussian mixture models and decision fusion techniques(Springer-Verlag Berlin, 2008) Benli, Kristin Surpuhi; Düzağaç, Remzi; Eskil, Mustafa TanerIn this paper we present our research in driver recognition. The goal of this study is to investigate the performance of different classifier fusion techniques in a driver recognition scenario. We are using solely driving behavior signals such as break and accelerator pedal pressure, engine RPM, vehicle speed; steering wheel angle for identifying the driver identities. We modeled each driver using Gaussian Mixture Models, obtained posterior probabilities of identities and combined these scores using different fixed mid trainable (adaptive) fusion methods. We observed error rates is low as 0.35% in recognition of 100 drivers using trainable combiners. We conclude that the fusion of multi-modal classifier results is very successful in biometric recognition of a person in a car setting.Yayın A quasi-one-dimensional bubbly cavitating flow model and comparison with experiments(European Turbomachinery Soc-Euroturbo, 2011) Delale, Can Fuat; Başkaya, Zafer; Pasinlioğlu, Şenay; Şen, Mete; Ayder, ErkanA bubbly cavitating flow model is constructed for unsteady quasi-one-dimensional and two-dimensional nozzle flows. In each case, the system of model equations is reduced to evolution equations for the flow velocity and bubble radius and the initial and boundary value problems of the evolution equations are formulated. The rest of the flow variables are then related to the solution of the evolution equations. Nozzle flow experiments are also carried out using water. The static wall pressures are measured at different locations of the nozzle and the partial cavitation cloud cycle is recorded using a high speed camera. Results of the numerical simulations obtained for quasi-one-dimensional nozzle flows, seem to capture the measured pressure losses due to cavitation, but they turn out to be insufficient in describing the two-dimensional cavitation cloud structures, suggesting the need for two-dimensional numerical solution of the model equations.
