Concept

Mean squared error

Related publications (588)

A strategy for adjusting combination weights over adaptive networks

This work proposes a strategy to adjust the combination weights of an adaptive network in order to attain both faster convergence during the transient phase and lower mean-square-error during the steady-state phase. Optimal combination weights are designed ...

IEEE2013

Beyond Moore-Penrose: Sparse Pseudoinverse

Martin Vetterli, Mihailo Kolundzija, Ivan Dokmanic

Frequently, we use the Moore-Penrose pseudoinverse (MPP) even in cases when we do not require all of its defining properties. But if the running time and the storage size are critical, we can do better. By discarding some constraints needed for the MPP, we ...

2013

Could electrical conductivity replace water level in rating curves for alpine streams?

Marc Parlange, Steven Vincent Weijs, Raphaël Mutzner

Streamflow time series are important for inference and understanding of the hydrological processes in alpine watersheds. Because streamflow is expensive to continuously measure directly, it is usually derived from measured water levels, using a rating curv ...

American Geophysical Union2013

On the Influence of Informed Agents on Learning and Adaptation Over Networks

Ali H. Sayed

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...

Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States2013

Analysis of linearization error for goal-oriented adaptivity of nonlinear problems

Luca Dede'

We propose in this talk to address the issue and effect of linearization in the quality of the error estimates in quantities of interest for strongly nonlinear problems. It is well known that the error representation in this case can be decomposed into two ...

International Center for Numerical Methods in Engineering (CIMNE)2013

Linear Interpolation Of Biomedical Images Using A Data-Adaptive Kernel

Michaël Unser, John Paul Ward, Aurélien Bourquard, Hagay Kirshner

In this work, we propose a continuous-domain stochastic model that can be applied to image data. This model is autoregressive, and accounts for Gaussian-type as well as for non-Gaussian-type innovations. In order to estimate the corresponding parameters fr ...

IEEE2013

Adaptive reduced basis finite element heterogeneous multiscale method

Assyr Abdulle, Yun Bai

An adaptive reduced basis finite element heterogeneous multiscale method (RB-FE-HMM) is proposed for elliptic problems with multiple scales. The multiscale method is based on the RB-FE-HMM introduced in [A. Abdulle, Y. Bai, Reduced basis finite element het ...

Elsevier2013

Sparse Distributed Learning Based on Diffusion Adaptation

Ali H. Sayed

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the d ...

IEEE2013

Intricacies of Describing Weak Interactions involving Halogen Atoms within Density Functional Theory

Ursula Röthlisberger, Ivano Tavernelli

In this work we assess the performance of different dispersion-corrected DFT approaches (M06, M06-2X, DFT-D3 and DCACP) in reproducing high-level wave function based benchmark calculations on the weakly bound halogen dimers (X2)2 and X2-Ar (for X=F,Cl,Br,I ...

Amer Chemical Soc2013

PREDIcT: Towards Predicting the Runtime of Large Scale Iterative Analytics

Anastasia Ailamaki, Adrian Daniel Popescu

Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...

2013

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