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Titlebook: Nonparametric Statistics; 3rd ISNPS, Avignon, Patrice Bertail,Delphine Blanke,Eric Matzner-L?ber Conference proceedings 2018 Springer Natu

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樓主: McKinley
51#
發(fā)表于 2025-3-30 10:42:13 | 只看該作者
A Nonparametric Classification Algorithm Based on Optimized Templates,ion from the centroid (prototype, template) of one of the groups. The general procedure is described on the particular task of mouth localization in facial images, where the centroid has the form of a mouth template. While templates are most commonly constructed as simple averages of positive exampl
52#
發(fā)表于 2025-3-30 14:52:24 | 只看該作者
53#
發(fā)表于 2025-3-30 19:31:17 | 只看該作者
54#
發(fā)表于 2025-3-30 20:49:38 | 只看該作者
55#
發(fā)表于 2025-3-31 02:58:13 | 只看該作者
56#
發(fā)表于 2025-3-31 08:58:32 | 只看該作者
57#
發(fā)表于 2025-3-31 13:16:14 | 只看該作者
Probability Bounds for Active Learning in the Regression Problem,sion problem simultaneously with that of model selection. We consider a batch type approach and an on-line approach adapting algorithms developed for the classification problem. Our main tools are concentration-type inequalities which allow us to bound the supreme of the deviations of the sampling s
58#
發(fā)表于 2025-3-31 14:42:57 | 只看該作者
Elemental Estimates, Influence, and Algorithmic Leveraging,be expressed as a weighted sum of so-called elemental estimates based on subsets of . observations where . is the dimension of parameter vector. The weights can be viewed as a probability distribution on subsets of size . of the predictors {..?:?.?=?1, ? , .}. In this contribution, we derive the low
59#
發(fā)表于 2025-3-31 17:39:23 | 只看該作者
60#
發(fā)表于 2025-3-31 22:29:50 | 只看該作者
Extension Sampling Designs for Big Networks: Application to Twitter,is of prohibitive cost for big graphs. Statistical estimators can thus be preferable. Model-based estimators for networks have some drawbacks. We study design-based estimates relying on sampling methods that were developed specifically for use on graph populations. In this contribution, we test some
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