Titlebook: Neural Information Processing; 24th International C Derong Liu,Shengli Xie,El-Sayed M. El-Alfy Conference proceedings 2017 Springer Interna

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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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0302-9743 e proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 ?full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on?Machine Learning,

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Fuzzy Self-Organizing Incremental Neural Network for Fuzzy Clusteringd due to the self-adjusting nodes and edges which fit the learning data incrementally. A removal of nodes and edges promises the robustness of the network to the noisy data. Experiments on artificial and real-world data prove the validity of the clustering method.

注射器

Topology Learning Embedding: A Fast and Incremental Method for Manifold Learninger way: it constructs a topology preserving network rapidly and incrementally through online input data; then with the Isomap-based embedding strategy, it achieves out-of-sample data embedding efficiently. Experiments on synthetic data and real-world handwritten digit data demonstrate that TLE is a promising method for dimensionality reduction.

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Using Flexible Neural Trees to Seed BackpropagationWe show that putting the two methods together can yield very good results. The FNT solution can be embedded into a larger neural network that is then optimized using backpropagation. The combination of the two methods outperforms either method alone.

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Improving Generalization Capability of Extreme Learning Machine with Synthetic Instances Generation based on 4 representative regression datasets of KEEL demonstrate that our proposed SIGELM obviously improves the generalization capability of ELM and effectively decreases the phenomenon of over-fitting.

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