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Titlebook: Computational and Statistical Methods in Intelligent Systems; Radek Silhavy,Petr Silhavy,Zdenka Prokopova Conference proceedings 2019 Spri

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樓主: Ford
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發(fā)表于 2025-3-28 17:59:02 | 只看該作者
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發(fā)表于 2025-3-28 21:17:16 | 只看該作者
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發(fā)表于 2025-3-28 23:06:41 | 只看該作者
Modeling Syntactic Structures of Vietnamese Complex Sentences,author proposes a new experimental method for patternizing syntactic structures of Vietnamese complex sentences. Specifically, 6 general structure classes for Vietnamese complex sentences have been patternized. After building and testing, the system is shown to have an accuracy of 82%–100%.
44#
發(fā)表于 2025-3-29 04:18:36 | 只看該作者
,Information Monitoring of Community’s Territoriality Based on Online Social Network,s is on possible methods for communities monitoring using the data of online social networks. The main research question is whether it is possible to investigate the real territorial characteristics of communities on the basis of their representations in a virtual environment? How accurately does th
45#
發(fā)表于 2025-3-29 10:56:07 | 只看該作者
Automated Extraction of Deontological Statements Through a Multilevel Analysis of Legal Acts, very complex task can be rather effectively solved in case when consideration is limited by certain specific kind of texts. The paper describes the developed technology of extraction of deontological statements from legal acts through a multilevel analysis of text documents combining linguistic and
46#
發(fā)表于 2025-3-29 11:42:25 | 只看該作者
47#
發(fā)表于 2025-3-29 18:53:12 | 只看該作者
,Spatio-Temporal Analysis of Macroeconomic Processes with Application to EU’s Unemployment Dynamics,ors, spill-over effects and the chronological autoregressive nature of observed variables need to be incorporated in macroeconomic modeling. This paper focuses on spatio-temporal aspects and dependencies in observed macroeconomic data. Corresponding quantitative analysis tools are outlined and an em
48#
發(fā)表于 2025-3-29 21:39:25 | 只看該作者
49#
發(fā)表于 2025-3-30 00:40:23 | 只看該作者
Enhancing Feature Selection with Density Cluster for Better Clustering,ional data. In this paper we propose a similarity metric based feature selection method named .. We use the Euclidean distance to measure the similarity among all features, and then apply the density based DBSCAN algorithm to clustering features which to be relevant. Moreover, we present a strategy
50#
發(fā)表于 2025-3-30 08:02:22 | 只看該作者
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