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 offered in all 3 volumes have been rigorously reviewed and chosen from 651 submissions. The contributions are dependent in topical sections on computational neuroscience and cognitive technology; neurodynamics and complicated platforms; balance and convergence research; neural community versions; supervised studying and unsupervised studying; kernel equipment and aid vector machines; mix types and clustering; visible belief and trend attractiveness; movement, monitoring and item attractiveness; average scene research and speech popularity; neuromorphic undefined, fuzzy neural networks and robotics; multi-agent structures and adaptive dynamic programming; reinforcement studying and choice making; motion and motor keep watch over; adaptive and hybrid clever platforms; neuroinformatics and bioinformatics; info retrieval; info mining and data discovery; and typical language processing.
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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Additional resources 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
Pattern Recognition Letters 31(3), 244–249 (2010) 18. : The Laplacian pyramid as a compact image code. IEEE Transactions on Communications COM 31(4), 532–540 (1983) 19. : A ﬁlter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing 40(4), 882–893 (1992) 20. : Translation-invariant contourlet transform and its application to image denoising. IEEE Transactions on Image Processing 15(11), 3362–3374 (2006) 21. html 22. MIT Vision and Modeling Group.
M. Valdovinos3, and E. Gasca4 1 3 Tecnol´ogico de Estudios Superiores de Jocotitl´an Carretera Toluca-Atlacomulco KM. 8, col. Ejido de San Juan y San Agust´ın, Jocotitl´an 2 Institute of New Imaging Technologies, Universitat Jaume I Av. 3, Col. Ma. Isabel, 56615 Valle de Chalco, Mexico 4 Lab. Reconocimiento de Patrones, Instituto Tecnol´ogico de Toluca Av. Tecnol´ogico s/n, 52140 Metepec, M´exico Abstract. The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process.
37–46, 2011. c Springer-Verlag Berlin Heidelberg 2011 38 P. Tiˇ no learning machines and sample sizes, there are situations where, by very nature of the problem, only extremely small samples are available. In such situations it is of utmost importance to theoretically analyze exactly what and under what circumstances can be learned. An example of such a scenario is detection of diﬀerentially expressed genes. One way in which biologists learn about diverse gene functionalities is the analysis of expression levels of selected genes in diﬀerent tissues, possibly obtained under diﬀerent conditions or treatment regimes.