Mean Squared Error Minimization for Inverse Moment Problems
نویسندگان
چکیده
منابع مشابه
Mean squared error minimization for inverse moment problems
We consider the problem of approximating the unknown density u ∈ L2(Ω, λ) of a measure μ on Ω ⊂ Rn, absolutely continuous with respect to some given reference measure λ, from the only knowledge of finitely many moments of μ. Given d ∈ N and moments of order d, we provide a polynomial pd which minimizes the mean square error ∫ (u− p)2dλ over all polynomials p of degree at most d. If there is no ...
متن کاملRoot Mean Squared Error
• Predictive Accuracy Measures. These measures evaluate how close the recommender system came to predicting actual rating/utility values. • Classification Accuracy Measures. These measures evaluate the frequency with which a recommender system makes correct/incorrect decisions regarding items. • Rank Accuracy Measures. These measures evaluate the correctness of the ordering of items performed b...
متن کاملCompetitive Mean-Squared Error Beamforming
Beamforming methods are used extensively in a variety of different areas, where one of their main goals is to estimate the source signal amplitude s(t) from the array observations y(t) = s(t)a + i(t) + e(t), t = 1,2,..., where a is the steering vector, i(t) is the interference, and e(t) is a Gaussian noise vector [1, 2]. To estimate s(t), we may use a beamformer with weights w so that s(t) = w*...
متن کاملOptimal Mean Squared Error Imaging
The problem of forming images that are optimal with respect to a Mean Square Error (MSE) criterion, based on nite data, is considered. First, it is shown that the MSE criterion is consistent with the general goal of classifying images, in that decreasing the MSE guarantees a decrease in the probability of misclassifying an image. The problem of choosing sampling locations for image formation th...
متن کاملLearning principal directions: Integrated-squared-error minimization
A common derivation of principal component analysis (PCA) is based on the minimization of the squared-error between centered data and linear model, corresponding to the reconstruction error. In fact, minimizing the squared-error leads to principal subspace analysis where scaled and rotated principal axes of a set of observed data, are estimated. In this paper, we introduce and investigate an al...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Mathematics & Optimization
سال: 2014
ISSN: 0095-4616,1432-0606
DOI: 10.1007/s00245-013-9235-z