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 established in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; photograph processing; details processing; neurocontrol; and novel applications.
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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
During training, information obtained by a new sub-network is merged together with the information obtained by the old network. After training, if outputs are collapsed with an additional network sitting on the top, links to the collapsed output units and all input units are built to collect more information from inputs, which is shown like that in Figure 2. Architecture in Figure 1 is called ILIA1, and the reformed network in Figure 2 is ILIA2. Although it is reported that ILIA2 is better than ILIA1, ILIA1 is still 32 T.
For training data from the ith ω i and the jth classes ω j , we solve the binary classification problem using PELM model. The reliability of classification Rc,k is calculated for each class c in each binary model k . Model k provides the reliability Ri , k that the unknown sample belongs to class ω i and also the reliability R j ,k that the sample belongs to class ω j . The combined reliability of classification of the sample in class c is calculated as Γc = ∏R ∏ R c,k C ∏R c,k (7) c,k c =1 where the numerator , only takes into account the reliability from the models that included class c.
The aim is to search for the optimal combination of measure function and feature subset satisfying the evaluation criterion. Training phase and testing phase are separated. Figure 1 shows the framework of our proposed approach. The inputs for the approach are the initial population of GA and the training data sets. The initial populations of chromosomes are constructed by a set of measure functions and their associated feature weights that are randomly initialized. All the samples are represented as vectors consisting of feature attributes and their corresponding category in the data set.