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Download Advances in Neural Networks – ISNN 2011: 8th International by Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang PDF

By Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)

The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed lawsuits of the eighth overseas Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers provided in all 3 volumes have been conscientiously reviewed and chosen from 651 submissions. The contributions are established in topical sections on computational neuroscience and cognitive technological know-how; neurodynamics and intricate platforms; balance and convergence research; neural community types; supervised studying and unsupervised studying; kernel equipment and help vector machines; mix types and clustering; visible notion and development popularity; movement, monitoring and item popularity; average scene research and speech acceptance; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent platforms and adaptive dynamic programming; reinforcement studying and determination making; motion and motor regulate; adaptive and hybrid clever platforms; neuroinformatics and bioinformatics; details retrieval; facts mining and information discovery; and common language processing.

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Read or Download Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II PDF

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Extra info for Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II

Example text

Each unit in the network starts from the output unit and it is moved to the hidden units. The error value is used to adjust the weights of its connections as well as to reduce the error. This process is repeated for a fixed number of times, or until the error is minimum or it cannot be reduced. Empirical studies of the back-propagation algorithm [11] show that class imbalance problem generates unequal contributions to the mean square error (MSE) in the training phase. Clearly the major contribution to the MSE is produced by the majority class.

Of samples 2500 3000 (b) Under-sampling Fig. 3. MLP outputs for the C03 subset, after apply the cost function and random under-sampling strategies. The line in black shows the separation between the outputs of both classes. tendency is observed in the MLP outputs, while in the Fig. 3b shows a more irregular behavior. However, the negative effects on the cls− are not very severe than the case of C04 subset. These results confirm that other problems, such as the overlap between classes or noise in the TDS, should be taken into account in classification tasks when the TDS is imbalanced [12, 13].

We randomly select 3 training samples from each of 40 classes, and let the other samples for test (that is, the test and training samples are separated). The ACAR is computed over the experimental results on 10 random splits of the training and test sets, which is listed in Table 1. 11%. In order to provide additional justification of our proposed method, we compare BPC+KNN (K = 1) with BPW+MD and M-band ridgelet transform based Fig. 4. The set of 40 texture images used in [7] 16 Y. Dong and J. Ma Table 1.

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