Titlebook: Discovery Science; 19th International C Toon Calders,Michelangelo Ceci,Donato Malerba Conference proceedings 2016 Springer International Pu

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書目名稱Discovery Science影響因子(影響力)




書目名稱Discovery Science影響因子(影響力)學(xué)科排名




書目名稱Discovery Science網(wǎng)絡(luò)公開度




書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Discovery Science被引頻次




書目名稱Discovery Science被引頻次學(xué)科排名




書目名稱Discovery Science年度引用




書目名稱Discovery Science年度引用學(xué)科排名




書目名稱Discovery Science讀者反饋




書目名稱Discovery Science讀者反饋學(xué)科排名

苦惱

Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototypexisting software, whereas more complex classifiers would require costly software adaptations. In order to predict a time series of instances, we construct a meta classification layer. We then evaluate our model on the data of 180 locomotive tours by leave one out classification. The results show tha

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https://doi.org/10.1007/978-3-658-01919-8of focusing on attribute subset selection, we explore an alternative promising approach consisting of using all available textual information. The problem of bug-fix time estimation is then mapped to a text categorization problem. We consider a multi-topic Supervised Latent Dirichlet Allocation (.)

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Second-Order-Faktorenanalyse (SFA)r law and the number of edges increase as a function of time. Therefore, we discuss a sequential sampling method with forgetting factor to sample the evolving ego network stream. This method captures the most active and recent nodes from the network while preserving the tie strengths between them an