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DAGSTUHL
2009
13 years 9 months ago
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Abstract. In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call su...
Erzsébet Merényi, Kadim Tasdemir, Li...
JAIR
2008
157views more  JAIR 2008»
13 years 8 months ago
Qualitative System Identification from Imperfect Data
Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to ...
George Macleod Coghill, Ashwin Srinivasan, Ross D....
BMCBI
2006
178views more  BMCBI 2006»
13 years 8 months ago
Correlation analysis of two-dimensional gel electrophoretic protein patterns and biological variables
Background: Two-dimensional gel electrophoresis (2DE) is a powerful technique to examine post-translational modifications of complexly modulated proteins. Currently, spot detectio...
Werner W. V. B. Van Belle, Nina N. A. Anensen, Ing...
NN
2006
Springer
13 years 8 months ago
Propagation and control of stochastic signals through universal learning networks
The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a s...
Kotaro Hirasawa, Shingo Mabu, Jinglu Hu
SIMPRA
2008
125views more  SIMPRA 2008»
13 years 8 months ago
Identification of Wiener models using optimal local linear models
The Wiener model is a versatile nonlinear block oriented model structure for miscellaneous applications. In this paper a method for identifying the parameters of such a model usin...
Martin Kozek, Sabina Sinanovic