GENUS 发表于 2025-3-23 11:09:08
Robust Bivariate Boxplots and Visualization of Multivariate Dataefine a natural, smooth and completely non parametric region in .. which retains the correlation in the data and adapts to differing spread in the various directions. In this paper we initially consider some variations of this method. The proposed approach shows some advantages with respect to that表状态 发表于 2025-3-23 17:42:02
Unsupervised Fuzzy Classification of Multispectral Imagery Using Spatial-Spectral Featuresd on purely spectral features. In our approach we additionally consider additional spatial features in the form of local context information. After all, spatial context is the defining property of an image. Markov random field modeling provides the assumption that the probability of a certain pixelDNR215 发表于 2025-3-23 20:38:10
http://reply.papertrans.cn/23/2273/227225/227225_13.png暂时别动 发表于 2025-3-23 23:40:44
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A New Look at the Visual Performance of Nonparametric Hazard Rate Estimators theory and application relates to the evaluation of the performance of the estimators. Recently, Marron and Tsybakov (1995) proposed . for addressing this issue of controversy in density estimation. Their core idea consists in using integrated alternatives to the Hausdorff distance for measuring thGEN 发表于 2025-3-24 06:39:05
https://doi.org/10.1007/978-3-031-59793-0ing strategy have been limited to the unidimensional case. Therefore, we present a comparison study using real data, which shows that the smoothing strategy performs better than three other strategies considered.hallow 发表于 2025-3-24 10:47:39
http://reply.papertrans.cn/23/2273/227225/227225_17.png匍匐 发表于 2025-3-24 16:43:03
Entropy Optimizing Methods for the Estimation of Tablesn RAS (or IPF) algorithm by allowing a wider class of constraints concerning the table entries such as equalities and inequalities over arbitrary cross sections. The theoretical background of the procedure is outlined and some examples of applications are reported.florid 发表于 2025-3-24 19:18:19
Christopher Leedham,Martin ScheureggerJoint correspondence analysis (JCA) is a commonly applied variation of multiple correspondence analysis (MCA) where the block-diagonal part of the Burt matrix is not considered in the fit. Examples shown here underline that this approach may in some cases lead to ambiguous results which may violate desirable properties of the representation.Legion 发表于 2025-3-25 03:09:11
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