نتایج جستجو برای: regularization parameter estimation

تعداد نتایج: 467554  

Journal: :Proceedings of the ... AAAI Conference on Artificial Intelligence 2023

Entropy regularization is known to improve exploration in sequential decision-making problems. We show that this same mechanism can also lead nearly unbiased and lower-variance estimates of the mean reward optimize-and-estimate structured bandit setting. Mean estimation (i.e., population estimation) tasks have recently been shown be essential for public policy settings where legal constraints o...

Journal: :Journal of nonparametric statistics 2015
Yoonsuh Jung Jianhua Hu

Cross-validation type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold cross validation procedure to select a candidate 'optimal' model from each hold-out fold and average the K candidate 'optimal' models to obtain the ultimate model. Due to the averaging effect, the variance of the proposed estimates can be significan...

Journal: :Signal Processing 2002
Masashi Sugiyama Hidemitsu Ogawa

Image restoration from degraded images lies at the foundation of image processing, pattern recognition, and computer vision, so it has been extensively studied. A large number of image restoration filters have been devised so far. It is known that a certain filter works excellently for a certain type of original image or degradation. However, the same filter may not be suitable for other images...

2006
Fabien Campillo Vivien Rossi

The state-space modeling of partially observed dynamic systems generally requires estimates of unknown parameters. From a practical point of view, it is relevant in such filtering contexts to simultaneously estimate the unknown states and parameters. Efficient simulation-based methods using convolution particle filters are proposed. The regularization properties of these filters is well suited,...

2009
Hong-jiang Wang Fei Ji Gang Wei Chi-Sing Leung

Regularization techniques have attracted many researches in the past decades. Most focus on designing the regularization term, and few on the optimal regularization parameter selection, especially for faulty neural networks. As is known that in the real world, the node faults often inevitably take place, which would lead to many faulty network patterns. If employing the conventional method, i.e...

2006
Petra Kudová

In this work we study and develop learning algorithms for networks based on regularization theory. In particular, we focus on learning possibilities for a family of regularization networks and radial basis function networks (RBF networks). The framework above the basic algorithm derived from theory is designed. It includes an estimation of a regularization parameter and a kernel function by min...

2015
Jodi L. Mead

Total Variation (TV) is an effective method of removing noise in digital image processing while preserving edges [23]. The choice of scaling or regularization parameter in the TV process defines the amount of denoising, with value of zero giving a result equivalent to the input signal. Here we explore three algorithms for specifying this parameter based on the statistics of the signal in the to...

Nowadays, navigation is an unavoidable fact in military and civil aerial transportations. The Global Positioning System (GPS) is commonly used for computing the orientation or attitude of a moving platform. The relative positions of the GPS antennas are computed using the GPS code and/or phase measurements. To achieve a precise attitude determination, Carrier phase observations of GPS requiring...

Journal: :SIAM J. Matrix Analysis Applications 2007
Amir Beck Yonina C. Eldar

We consider the problem of estimating a vector z in the regression model b = Az+w, where w is an unknown but bounded noise. As in many regularization schemes, we assume that an upper bound on the norm of z is available. To estimate z we propose a relaxation of the Chebyshev center, which is the vector that minimizes the worst-case estimation error over all feasible vectors z. Relying on recent ...

2010
Mark Culp George Michailidis Kjell Johnson

Boosting algorithms build models on dictionaries of learners constructed from the data, where a coefficient in this model relates to the contribution of a particular learner relative to the other learners in the dictionary. Regularization for these models is currently implemented by iteratively applying a simple local tolerance parameter, which scales each coefficient towards zero. Stochastic e...

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