Stochastic Configuration Networks: Fundamentals and Algorithms

Ranking algorithms on directed configuration networks

This paper studies the distribution of a family of rankings, which includes Google’s PageRank, on a directed configuration model. In particular, it is shown that the distribution of the rank of a randomly chosen node in the graph converges in distribution to a finite random variable R∗ that can be written as a linear combination of i.i.d. copies of the endogenous solution to a stochastic fixed ...

Fundamentals of Computer Algorithms

Fundamentals of algorithms

In this chapter, we will go through the fundamentals of algorithms that are essential for the readers to appreciate the beauty of various EDA technologies covered in the rest of the book. For example, many of the EDA problems can be either represented in graph data structures or transformed into graph problems. We will go through the most representative ones in which the efficient algorithms ha...

Stochastic simulation algorithms for dynamic probabilistic networks

Stochastic simulation algorithms such as like­ lihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfor­ tunately, the special characteristics of dy­ namic probabilistic networks (DPNs), which are used to represent stochastic temporal pro­ cesses, mean that standard simulation alg...

Building Robust Stochastic Configuration Networks with Kernel Density Estimation

Neural networks have been widely used as predictive models to fit data distribution, and they could be implemented through learning a collection of samples. In many applications, however, the given dataset may contain noisy samples or outliers which may result in a poor learner model in terms of generalization. This paper contributes to a development of robust stochastic configuration networks ...