Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern ...

In this paper, we present a method that uses panoramic images to perform long-range navigation as a succession of short-range homing steps along a route specified by appearances of the environment of the robot along the ...

This thesis documents the investigation into the acquisition of knowledge from biological data using computational methods for the discovery of significantly frequent patterns in gene location and phylogeny. Beginning ...

Automotive electrical and electronic systems have become very sophisticated in a relatively short time. This complexity has both led to the increased need for design analysis tools and the need for these tools to deal with ...

Changes in the degree of polarisation are described by a single Mueller matrix. Both the monochromatic and quasimonochromatic cases are discussed. The authors show how the propagating degree of polarisation depends on the ...

For some time, learning Bayesian networks has been both feasible and useful in many problems domains. Recently research has been done on learning equivalence classes of Bayesian networks, i.e. structures that capture all ...

Developmental psychology has long recognized the presence of stages in human cognitive development, although the underlying causes and processes are still an open question and subject to much debate. This article draws ...

Motivation: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition ...