We describe a technique that allows the exact analytical computation of the mean first passage time (MFPT) for infinite families of trees using their recursive properties. The method is based in the relationship between the MFPT and the eigenvalues of the Laplacian matrix of the trees but avoids their explicit computation. We apply this technique to find the MFPT for a family of generalized det...

Many algorithms have been developed to generate sequences for trees, and a few are to generate trees themselves, i.e., in computer representation. Two new algorithms are presented in this paper to generate k-ary trees in computer representation. One is recursive with constant average time per tree and the other is loopless (non-recursive) with constant time per tree. Both of them are simple and...

Recursive neural networks are a new connec-tionist model recently introduced for processing graphs. Linear recursive networks are a special sub-class where the neurons have linear activation functions. The approximation properties of recursive networks are tightly connected to the possibility of distinguishing the patterns by generating a different internal encoding for each input of the domain...

The advent of gene chips has led to a promising technology for cell, tumor, and cancer classification. We exploit and expand the methodology of recursive partitioning trees for tumor and cell classification from microarray gene expression data. To improve classification and prediction accuracy, we introduce a deterministic procedure to form forests of classification trees and compare their perf...

This thesis develops the novel method of recursive cavity modeling as a tractable approach to approximate inference in large Gauss-Markov random fields. The main idea is to recursively dissect the field, constructing a cavity model for each subfield at each level of dissection. The cavity model provides a compact yet (nearly) faithful model for the surface of one subfield sufficient for inferri...

Random code-trees with necks were introduced recently to generalise the notion of $V$-variable and random homogeneous sets. While it is known that Hausdorff packing dimensions coincide irrespective overlaps, their exact measure has so far been largely ignored. In this article we consider general question an appropriate gauge function for positive finite measure. We first survey current state kn...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...