Download Advances in Neural Networks – ISNN 2012: 9th International by Dazhong Ma, Jinhai Liu, Zhanshan Wang (auth.), Jun Wang, PDF

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 awarded have been rigorously reviewed and chosen from a number of submissions. The contributions are dependent in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development acceptance; imaginative and prescient; picture processing; info processing; neurocontrol; and novel applications.

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Read 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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Additional info for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part II

Example text

The dataset was divided into two groups of training and testing sets: 444 samples for training and 297 samples for testing. In the training period, input and output dataset of three classes X ω1 ∈ R 161×9 are , Xω 2 ∈ R Xω1 ∈ R 161×9 , Xω 2 ∈ R 175×9 , Xω 3 ∈ R 108×9 175×9 , Xω 3 ∈ R scaled to zero mean and unit variance. 108×9 , and for the testing period. All the data were Multi-class Classification with One-Against-One Using Probabilistic Extreme Learning 15 Table 1. 2 Multi-class Classification In the study, three states are considered.

This paper proposes a novel approach to search for the optimal combination of a measure function and feature weights using an evolutionary algorithm. Different combinations of measure function and feature weights are used to construct the searching space. Genetic Algorithm is applied as an evolutionary algorithm to search for the candidate solution, in which the classification rate of the K-Nearest Neighbor classifier is used as the fitness value. Three experiments are carefully designed to show the attractiveness of our approach.

The size of the data sets ranges from 101 to 2310 and there are 2-class data sets and also multi-class data sets. 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).

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