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 court cases of the eighth foreign Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011.
The overall of 215 papers awarded 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 versions; supervised studying and unsupervised studying; kernel equipment and help vector machines; combination versions and clustering; visible belief and trend popularity; movement, monitoring and item acceptance; ordinary scene research and speech popularity; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent structures and adaptive dynamic programming; reinforcement studying and selection making; motion and motor regulate; adaptive and hybrid clever structures; neuroinformatics and bioinformatics; info retrieval; info mining and data discovery; and common language processing.
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Additional 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
In our previous study (BMC Bioinformatics, 2009) we theoretically analyzed one of the most popular techniques for identifying genes with statistically diﬀerent expression in SAGE libraries - the Audic-Claverie statistic (Genome Research, 1997). When comparing two libraries in the Audic-Claverie framework, it is assumed that under the null hypothesis their tag counts come from the same underlying (unknown) Poisson distribution. Since each SAGE library represents a single measurement, the inference has to be performed on the smallest sample possible - sample of size 1.
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National Research Council. Washington, DC. Urban Stormwater Management in the United States, pp. 18–20 (2008) 3. Fact Sheet 102-98 - The Chesapeake Bay: Geologic Product of Rising Sea Level. U. S. gov/fs/fs102-98/ 4. : Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998) 5. : Rainfall-runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed). American-Eurasian J. Agric. & Environ. Sci. 5(6), 856–865 (2009) 6. : RBF Neural Network Model Based on Improved PSO for Predicting River Runoff.
Advances in Neural Networks – ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, Part II by Bo Li, Jin Liu, Wenyong Dong (auth.), Derong Liu, Huaguang Zhang, Marios Polycarpou, Cesare Alippi, Haibo He (eds.)