Titlebook: Advances in Knowledge Discovery and Data Mining; 23rd Pacific-Asia Co Qiang Yang,Zhi-Hua Zhou,Sheng-Jun Huang Conference proceedings 2019 S

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書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)




書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度




書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining年度引用




書目名稱Advances in Knowledge Discovery and Data Mining年度引用學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)科排名

Meager

heirloom

遺棄

https://doi.org/10.1007/978-3-030-04972-0nities. Existing QA studies assume that questions are raised by humans and answers are generated by machines. Nevertheless, in many real applications, machines are also required to determine human needs or perceive human states. In such scenarios, machines may proactively raise questions and humans

果仁

Coupling in UAV Cooperative Control, of the mainstream approaches to tackle this task. However, most of the existing studies focus on some specific kind of auxiliary data, which is usually platform- or domain- dependent. In existing works, the incorporation of auxiliary data has put limits on the applicability of the prediction model

排他

https://doi.org/10.1007/978-3-319-74265-6 smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence

集合

https://doi.org/10.1007/978-3-319-74265-6with the equipment’s types. Proceeding from the fundamental features of load time series, we propose a method to identify electrical equipment from power load profiles accurately. Aiming to improve the classification accuracy and generalization performance of convolutional neural network (CNN), we c

階層

枯萎將要

https://doi.org/10.1007/978-3-319-74265-6er architecture, have achieved impressive progress in abstractive document summarization. However, the saliency of summary, which is one of the key factors for document summarization, still needs improvement. In this paper, we propose Topic Attentional Neural Network (TANN) which incorporates topic

surmount