Titlebook: Neural Information Processing; 22nd International C Sabri Arik,Tingwen Huang,Qingshan Liu Conference proceedings 2015 Springer Internationa

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书目名称Neural Information Processing影响因子(影响力)




书目名称Neural Information Processing影响因子(影响力)学科排名




书目名称Neural Information Processing网络公开度




书目名称Neural Information Processing网络公开度学科排名




书目名称Neural Information Processing被引频次




书目名称Neural Information Processing被引频次学科排名




书目名称Neural Information Processing年度引用




书目名称Neural Information Processing年度引用学科排名




书目名称Neural Information Processing读者反馈




书目名称Neural Information Processing读者反馈学科排名

拍下盗公款

Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM,et partition to increment classifier model performance. We applied Gini impurity approach to find the best split percentage of noise filter ratio. The filtered sub data set is then used to train individual ensemble models.

错事

Weighted ANN Input Layer for Adaptive Features Selection for Robust Fault Classification,lue vector. Different instances of ANN are then trained and tested to calculate F1_score with the reduced dominant features at different SNRs for each threshold value. Trained ANN with best average classification accuracy among all ANN instances gives us required number of dominant features.

陶瓷

Neural Network with Evolutionary Algorithm for Packet Matching,ative procedure. Data experiments show that this new algorithm effectively improves the performance of packet matching compared with the classical algorithms. And it can completely solve the problem of large-scale rule packet matching.

慷慨援助

A Parallel Sensitive Area Selection-Based Particle Swarm Optimization Algorithm for Fast Solving CNate the validity, we take Zebiak-Cane (ZC) numerical model as a case. Experimental results show that the proposed method can obtain a better CNOP more efficiently than SAEP [.] and PCAGA [.] which are two latest researches on intelligent algorithms for solving CNOP.

阻塞

Semi-supervised Non-negative Local Coordinate Factorization,led examples to be the class indicator. Benefit from the labeled data, SNLCF can boost NMF in clustering the unlabeled data. Experimental results on UCI datasets and two popular face image datasets suggest that SNLCF outperforms the representative methods in terms of both average accuracy and average normalized mutual information.

隐士

连词

Trading Optimally Diversified Portfolios in Emerging Markets with Neuro-Particle Swarm Optimisationo diversity) and that in the case of emerging markets the optimal value for this parameter may be different to the standard investment industry recommendation. Learning is then extended to include this parameter, with out-of-sample testing demonstrating very promising results.

现存

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