246,086 research outputs found
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
The sum of edge lengths in random linear arrangements
Spatial networks are networks where nodes are located in a space equipped
with a metric. Typically, the space is two-dimensional and until recently and
traditionally, the metric that was usually considered was the Euclidean
distance. In spatial networks, the cost of a link depends on the edge length,
i.e. the distance between the nodes that define the edge. Hypothesizing that
there is pressure to reduce the length of the edges of a network requires a
null model, e.g., a random layout of the vertices of the network. Here we
investigate the properties of the distribution of the sum of edge lengths in
random linear arrangement of vertices, that has many applications in different
fields. A random linear arrangement consists of an ordering of the elements of
the nodes of a network being all possible orderings equally likely. The
distance between two vertices is one plus the number of intermediate vertices
in the ordering. Compact formulae for the 1st and 2nd moments about zero as
well as the variance of the sum of edge lengths are obtained for arbitrary
graphs and trees. We also analyze the evolution of that variance in Erdos-Renyi
graphs and its scaling in uniformly random trees. Various developments and
applications for future research are suggested
Explosive Percolation: Unusual Transitions of a Simple Model
In this paper we review the recent advances on explosive percolation, a very
sharp phase transition first observed by Achlioptas et al. (Science, 2009).
There a simple model was proposed, which changed slightly the classical
percolation process so that the emergence of the spanning cluster is delayed.
This slight modification turns out to have a great impact on the percolation
phase transition. The resulting transition is so sharp that it was termed
explosive, and it was at first considered to be discontinuous. This surprising
fact stimulated considerable interest in "Achlioptas processes". Later work,
however, showed that the transition is continuous (at least for Achlioptas
processes on Erdos networks), but with very unusual finite size scaling. We
present a review of the field, indicate open "problems" and propose directions
for future research.Comment: 27 pages, 4 figures, Review pape
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