Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

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書目名稱Neural Information Processing影響因子(影響力)




書目名稱Neural Information Processing影響因子(影響力)學(xué)科排名




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度




書目名稱Neural Information Processing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Neural Information Processing被引頻次




書目名稱Neural Information Processing被引頻次學(xué)科排名




書目名稱Neural Information Processing年度引用




書目名稱Neural Information Processing年度引用學(xué)科排名




書目名稱Neural Information Processing讀者反饋




書目名稱Neural Information Processing讀者反饋學(xué)科排名

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Neural Information Processing978-3-030-04221-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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A Multi-indicator Feature Selection for CNN-Driven Stock Index Predictional applications, different indicator features have significant impact when predicting stock index. In addition, different technical indicators which contained in the same matrix will interfere with each other when convolutional neural network (CNN) is applied to feature extraction. To solve the abov

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Uplift Prediction with Dependent Feature Representation in Imbalanced Treatment and Control Conditio these past years. In this paper, we consider a typical situation where the learner has access to an imbalanced treatment and control data collection affecting the performance of the existing approaches. Inspired from transfer and multi-task learning paradigms, our approach overcomes this problem by

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Applying Macroclimatic Variables to Improve Flow Rate Forecasting Using Neural Networks Techniques has being necessary to operate at a minimal flow rate. It is far below the ones established at the operation licence, which is 1300 m./s. Due to this hydraulic crisis, the actual operational flow rate is 700 m./s at S?o Francisco River, characterizing this situation as critical. In this work, it wa