稀少 发表于 2025-3-21 16:38:49

书目名称Discovery Science影响因子(影响力)<br>        http://figure.impactfactor.cn/if/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science影响因子(影响力)学科排名<br>        http://figure.impactfactor.cn/ifr/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science网络公开度<br>        http://figure.impactfactor.cn/at/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science网络公开度学科排名<br>        http://figure.impactfactor.cn/atr/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science被引频次<br>        http://figure.impactfactor.cn/tc/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science被引频次学科排名<br>        http://figure.impactfactor.cn/tcr/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science年度引用<br>        http://figure.impactfactor.cn/ii/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science年度引用学科排名<br>        http://figure.impactfactor.cn/iir/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science读者反馈<br>        http://figure.impactfactor.cn/5y/?ISSN=BK0281043<br><br>        <br><br>书目名称Discovery Science读者反馈学科排名<br>        http://figure.impactfactor.cn/5yr/?ISSN=BK0281043<br><br>        <br><br>

不如屎壳郎 发表于 2025-3-21 23:45:28

Recent Developments in Pattern Miningquent itemsets have been proposed. These exhaustive algorithms, however, all suffer from the pattern explosion problem. Depending on the minimal support threshold, even for moderately sized databases, millions of patterns may be generated. Although this problem is by now well recognized in te patter

同音 发表于 2025-3-22 02:15:23

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stratum-corneum 发表于 2025-3-22 05:21:14

Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solverse price for this promise is the computational cost .(..) for computing the eigen-decomposition of the graph Laplacian matrix - so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced

COMA 发表于 2025-3-22 09:49:45

Prediction of Quantiles by Statistical Learning and Application to GDP Forecastingnctions. In a first time, we show that the Gibbs estimator is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of , this allows to build confidence intervals. We apply these results to the problem of

Robust 发表于 2025-3-22 15:41:12

Policy Search in a Space of Simple Closed-form Formulas: Towards Interpretability of Reinforcement Llgorithm over a space of simple closed-form formulas that are used to rank actions. We formalize the search for a high-performance policy as a multi-armed bandit problem where each arm corresponds to a candidate policy canonically represented by its shortest formula-based representation. Experiments

Robust 发表于 2025-3-22 17:46:02

Towards Finding Relational Redescriptionstional dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. As a first step towards solving this general task, we introduce an efficient algorithm that mines one descrip

CRP743 发表于 2025-3-22 22:06:50

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自爱 发表于 2025-3-23 01:51:26

A Trim Distance between Positions in Nucleotide Sequencesg the indices of nucleotide sequences as labels of leaves with the nucleotides occurring in a position, we formulate a . between two positions in nucleotide sequences as the LCA-preserving distance between the trimmed phylogenetic trees according to nucleotides occurring in the positions. Finally, w

gastritis 发表于 2025-3-23 05:39:03

Data Squashing for HSV Subimages by an Autonomous Mobile Robotakes during a navigation of dozens of minutes. The subimages are managed according to a similarity measure between a pair of subimages, which is based on a method for quantizing HSV colors. The data index structure has been inspired by the CF tree of BIRCH, which is an early work in data squashing,
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查看完整版本: Titlebook: Discovery Science; 15th International C Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Pet Conference proceedings 2012 Springer-Verlag Berl