[PDF] Top 20 Factores de riesgo asociados con la maloclusión
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Minimax Estimation of Kernel Mean Embeddings
... the kernel), one might wonder whether the minimax rate can be improved by restricting P to distributions with smooth ...the minimax lower bound of order n −1/2 ...be embeddings of the ... See full document
11
Robust Kernel Density Estimation
... Density estimation with positive semi-definite kernels has been studied by several ...space embeddings of probability ...the mean of the empirical distribution in the RKHS, but our criterion is ... See full document
11
Kernel density estimation on the torus
... exact mean integrated squared error for the proposed density derivatives estimator and, finally, we discuss its minimax ...the mean integrated squared error, a new smoothing concept arises, according ... See full document
40
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
... the kernel ridge regression estimate and discussing the assumptions that underlie our ...the mean-squared error between the averaged estimate ¯ f and the optimal regression function f ∗ ...the ... See full document
47
Noise Reduction Techniques for Processing of Medical Images
... on kernel size and σ some of coefficients can be out range of ...the mean. Thus it is possible to truncate the kernel size at this ...Sometimes kernel size truncated even ...Gaussian ... See full document
137
Some Improvement on Convergence Rates of Kernel Density Estimator
... the kernel estimators are calculated and ...of kernel estimators do not de- pend much on which particular kernel is used, we choose the standard normal as the kernel function K without loss of ... See full document
108
Adaptive Minimax Regression Estimation over Sparse $\ell_q$-Hulls
... optimal estimation that allows model misspec- ification is done in the framework of aggregation of ...the minimax rate of aggregation is properly determined by find- ing a specific set of initial estimates ... See full document
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Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy
... proposed estimation method for combining biomarkers in a number of simulated ...proposed kernel machine estimation (KME) method is compared against some existing popular alternatives, including the ... See full document
6
State-by-state Minimax Adaptive Estimation for Nonparametric Hidden Markov Models
... as minimax rates of convergence (in L 2 norm) are concerned, however this can become an issue if one wants to use these densities in a forward-backward algorithm in order to get an estimator of the hidden ...the ... See full document
16
Semi- Minimax Estimation of the Scale Parameter of Laplace Distribution under Symmetric and Asymmetric Loss Functions
... Semi- minimax estimation is an upgraded non- classical approach in the estimation area of statistical ...Semi- minimax approach is the specification of the distribution function on the ... See full document
16
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
... cardinal data obtained from the experiments into ordinal form by comparing answers given by the subjects to consecutive questions. For five of the experiments ((a) through (e)), we had access to the “ground truth” ... See full document
79
Estimating a Bounded Normal Mean Under the LINEX Loss Function
... normal mean in the case where is bounded below, ...literature. Estimation of a positive normal mean was first considered in Katz ...normal mean has developed in the literature, see for ... See full document
11
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization
... Overall, we obtained better results than the n- gram baselines, DTK and VTK, especially with syntactic phrases. VTK shows good performance across all datasets but its computation was more than 700% slower than with our ... See full document
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The trade-off between fix rate and tracking duration on estimates of home range size and habitat selection for small vertebrates
... range estimation often with VHF not GPS tags [25,29], rather than effects on estimates of habitat selection ...range estimation, whilst Huck [20], Walter [33] and Byer [34] identified method of ... See full document
17
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
... An important feature of our analysis is we assume only that each univariate function is bounded, but do not assume that the multivariate function class is bounded. As discussed in Section 3.5, imposing a global ... See full document
13
On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models*
... Sections 1.1 and 2 concern various aspects of Bayesian nonparametric or semiparametric estimation proce- dures. In the following section we describe another aspect of Bayesian nonparametric inference, namely the ... See full document
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Kernel Mean Shrinkage Estimators
... A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel ... See full document
84
Minimax estimation of qubit states with Bures risk
... a minimax estimator under the Hellinger distance, we would like to make some comments on this problem, emphasising that it is crucial to study what happens at the boundary when λ ≈ ...asymptotic minimax ... See full document
27
Suggesting Sentences for ESL using Kernel Embeddings
... We show the error analysis on the 19-gram RBF kernel, which exhibited the best score in the ex- perimental results. Table 2 presents examples of one of the top-ten results of sentence retrieval. The topmost ... See full document
28
Improving Word Embeddings Using Kernel PCA
... The results for the 20 Newsgroups dataset in Table 4 also show that the skip-gram models completely fail to learn analogies on this dataset. The KPCA fastText embeddings benefit from their warm-start and show a ... See full document
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