By Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)
The two-volume set LNCS 7367 and 7368 constitutes the refereed court cases of the ninth overseas Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers provided have been conscientiously reviewed and chosen from various submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; photograph processing; info processing; neurocontrol; and novel applications.
Read Online or Download Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II PDF
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Extra info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II
In the second experiment, data sets from the University of California at Irvine are employed to verify the general applicability of the method. Finally, a prostate cancer data set is used to show its eﬀectiveness on high-dimensional data. Keywords: feature selection, measure function, genetic algorithm. 1 Introduction The similarity measure of two patterns and feature selection are the critical factors aﬀecting the performance of classiﬁer, especially for classiﬁcation based on pattern similarity theory.
Table 3 gives the name of the data sets. Five comparable experiments were designed step by step. First, Normalized KNN (marked as NKNN) was used as the classiﬁer, with EuM and all features had equal weight 1. Second, feature selection only is applied. The MhM and the EuM are respectively used in the binary encoding mode (marked as BW KNN) and the real value encoding mode (marked as RW KNN). Third, our proposed method was applied. 0, were supplied to construct the initial population of chromosomes (marked as MF RW KNN).
System, Man, and Cybernetics-Part A: Systems and Humans 40(3), 641–650 (2010) 15. : A fuzzy min-max neural network classiﬁer with compensatory neuron architecture. IEEE Trans. Neural Networks 18(1), 42–54 (2007) 16. : Data-Core-Based Fuzzy Min-Max Neural Network for Pattern Classiﬁcation. IEEE Trans. Neural Networks 22(12), 2339–2352 (2011) 17. com Abstract. Probabilistic extreme learning machine (PELM) is a binary classification method, which can improve the computational speed, generalization performance and computational cost.