The Editor,Technometrics

Following an analogous distinction in statistical hypothesis testing and motivated by chemical plume detection in hyperspectral imagery, we investigate machine-learning algorithms where the training set is comprised of matched pairs. We find that even conventional classifiers exhibit improved performance when the input data have a matched-pair structure, and we develop an example of a “dipole” algorithm to directly exploit this structured input. In some scenarios, matched pairs can be generated from independent samples, with the effect of not only doubling the nominal size of the training set, but of providing the matched-pair structure that leads to better learning. The creation of matched pairs from a dataset of interest also permits a kind of transductive learning, which is found for the plume detection problem to exhibit improved performance. Supplementary materials for this article are available online.     

The 1983 Technometrics Prizes

This article discusses three well-known methods for obtaining a graphical evaluation of the effect of adding an explanatory variable in linear regression. A new method is also proposed.     

The 1980 Technometrics Prizes

In classification methods that explicitly model class-conditional probability distributions, the true distributions are often not known. These are estimated from the data available, to approximate the true distributions. Errors in classification that arise due to this approximation can be reduced to some extent if the estimated distributions are used merely to project data into a space of likelihoods and classification is performed in that space using discriminant functions. In this article, we discuss the rationale behind this, and also the general properties of likelihood projections. We demonstrate the utility of likelihood projections in improving classification performance through experiments carried out on a standard image database and a standard speech database.