Disseminated tumor cells often fall into a long term of dormant stage, characterized by
decreased proliferation but sustained survival, in distant organs before awakening for …

A novel, unsupervised nonparametric model of multivariate probability density functions
(pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to …

Structured data in the form of labeled graphs (with variable order and topology) may be
thought of as the outcomes of a random graph (RG) generating process characterized by an …

Estimation of probability density functions is an essential component of various applications.
Nonparametric techniques have been widely used for this task owing to the difficulty in …

The paper introduces a robust connectionist technique for the empirical nonparametric
estimation of multivariate probability density functions (pdf) from unlabeled data samples …

The Thermodynamic Formalism provides a rigorous mathematical framework for studying
quantitative and qualitative aspects of dynamical systems. At its core, there is a variational …

We propose a new way to measure the balance between freedom and coherence in a
dynamical system and a new measure of its internal variability. Based on the concept of …

Let (X, G) be a G-system, where G is an infinite countable discrete amenable group and X is
a compact metric space with a metric d. In this paper, we study the topological pressure of …

The estimation of probability density functions (pdf) from unlabeled data samples is a
relevant (and, still open) issue in pattern recognition and machine learning. Statistical …

This paper develops a free energy theory from physics including the variational principles for
automata and languages and also provides algorithms to compute the energy as well as …