One of the most frequently used models for classification tasks is the Probabilistic Neural Network. Several improvements of the Probabilistic Neural Network have been proposed such as the Evolutionary Probabilistic Neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To furthe...

Evolutionary algorithms appear as an interesting alternative to achieve minimal error rates and low numbers of rules in supervised learning tasks. In spite of the computational cost of this approach, some proposals can be applied to make the algorithm faster and more efficient. This paper describes some of these proposals, which are integrated in the evolutionary tool HIDER∗. Specifically, we d...

We consider the mining of hidden block structures from time-varying data using evolutionary co-clustering. Existing methods are based on the spectral learning framework, thus lacking a probabilistic interpretation. To overcome this limitation, we develop a probabilistic model for evolutionary co-clustering in this paper. The proposed model assumes that the observed data are generated via a two-...

Probabilistic Methods for Multiscale Evolutionary Dynamics by Shishi Luo Department of Mathematics Duke University

In this paper we study two models of labelled random trees that generalise the original unlabelled Schröder tree. Our new can be seen as for phylogenetic in which nodes represent species and labels encode order appearance these species, thus chronology evolution. One important feature our is they generated efficiently thanks to a dynamical, recursive construction. first model an increasing tree...

Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurre...