Titlebook: Advances in Neural Networks – ISNN 2012; 9th International Sy Jun Wang,Gary G. Yen,Marios M. Polycarpou Conference proceedings 2012 Springe

incompatible

書目名稱Advances in Neural Networks – ISNN 2012影響因子(影響力)




書目名稱Advances in Neural Networks – ISNN 2012影響因子(影響力)學科排名




書目名稱Advances in Neural Networks – ISNN 2012網(wǎng)絡公開度




書目名稱Advances in Neural Networks – ISNN 2012網(wǎng)絡公開度學科排名




書目名稱Advances in Neural Networks – ISNN 2012被引頻次




書目名稱Advances in Neural Networks – ISNN 2012被引頻次學科排名




書目名稱Advances in Neural Networks – ISNN 2012年度引用




書目名稱Advances in Neural Networks – ISNN 2012年度引用學科排名




書目名稱Advances in Neural Networks – ISNN 2012讀者反饋




書目名稱Advances in Neural Networks – ISNN 2012讀者反饋學科排名

COUCH

alcoholism

https://doi.org/10.1007/978-3-642-31346-2algorithms; extreme learning machines; feature selection; fuzzy neural networks; machine learning; algori

Deadpan

DAUNT

Jun Wang,Gary G. Yen,Marios M. PolycarpouUp to date results.State of the art research.Fast track conference proceedings

襲擊

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/149164.jpg

細胞學

ANIM

M. Joseph Sirgy,Rhonda Phillips,Don R. Rahtz improve the efficacy of neural network training. However, those global algorithms suffer the curse of dimensionality. We propose a new approach that focuses on the topology of the solution space. Our method prunes the search space by using the Lipschitzian property of the criterion function. We hav

過分

achlorhydria

https://doi.org/10.1007/978-94-007-0535-7based on mutual information and extreme learning machines is proposed in this paper. Simple mutual information based feature selection method is integrated with the fast learning kernel based extreme learning machines to obtain better modeling performance. In the method, optimal number of the featur