Neural network ensemble techniques have been shown to be very accurate classification techniques. However, in some real-life applications a number of classifiers required to achieve a reasonable accuracy is enormously large and hence very space consuming. This paper proposes several methods for pruning neural network ensembles. The clustering based approach applies k-means clustering to entire ...

UNLABELLED The energy landscape of RNA secondary structures is often complex, and the Boltzmann-weighted ensemble usually contains distinct clusters. Furthermore, the minimum free energy structure often lies outside of the cluster containing the structure determined by comparative sequence analysis. We have developed procedures to characterize and visualize the Boltzmann-weighted ensemble, and ...

The new Theorem on location of maximum of probability density functions of dimensionless second difference of the three adjacent energy levels for N -dimensional Gaussian orthogonal ensemble GOE(N), N -dimensional Gaussian unitary ensemble GUE(N), N -dimensional Gaussian symplectic ensemble GSE(N), and Poisson ensemble PE, is formulated: The probability density functions of the dimensionless se...

Facing a large number of clustering solutions, cluster ensemble method provides an effective approach to aggregating them into a better one. In this paper, we propose a novel cluster ensemble method from probabilistic perspective. It assumes that each clustering solution is generated from a latent cluster model, under the control of two probabilistic parameters. Thus, the cluster ensemble probl...