Signal Detection by Human Observers

Contains a report on a research project.This work was supported in part by United States Air Force (Contract AF19(604)-1728

Signal Detection by Human Observers

Contains research objectives and reports on one research project

Signal Detection by Human Observers

Contains research objectives and reports on one research project.U.S. Air Force Contract AF19(604)-1728, monitored by the Operational Applications Laboratory, Air Force Cambridge Research Cente

Signal Detection by Human Observers

Contains research objectives.U. S. Air Force Contract AF19(604)-7459, monitored by Operations Analysis Office, Air Force Command and Control Development Division, Bedford, Massachusett

Signal Detection by Human Observers

Contains reports on three research projects.United States Air Force (Contract AF19(604)-1728

Children view own-age faces qualitatively differently to other-age faces

ike most own-group biases in face recognition, the own-age bias (OAB) is thought to be based either on perceptual expertise or socio-cognitive motivational mechanisms [Wolff, N., Kemter, K., Schweinberger, S. R., & Wiese, H. (2013). What drives social in-group biases in face recognition memory? ERP evidence from the own-gender bias. Social Cognitive and Affective Neuroscience. doi:10.1093/scan/nst024]. The present study employed a recognition paradigm with eye-tracking in order to assess whether participants actively viewed faces of their own-age differently to that of other-age faces. The results indicated a significant OAB (superior recognition for own-age relative to other-age faces), provided that they were upright, indicative of expertise being employed for the recognition of own-age faces. However, the eye-tracking results indicate that viewing other-age faces was qualitatively different to the viewing of own-age faces, with more nose fixations for other-age faces. These results are interpreted as supporting the socio-cognitive model of the OAB

A survey of cost-sensitive decision tree induction algorithms

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms including approaches that are direct adaptations of accuracy based methods, use genetic algorithms, use anytime methods and utilize boosting and bagging. The survey brings together these different studies and novel approaches to cost-sensitive decision tree learning, provides a useful taxonomy, a historical timeline of how the field has developed and should provide a useful reference point for future research in this field

Loss-size and Reliability Trade-offs Amongst Diverse Redundant Binary Classifiers

Many applications involve the use of binary classifiers, including applications where safety and security are critical. The quantitative assessment of such classifiers typically involves receiver operator characteristic (ROC) methods and the estimation of sensitivity/specificity. But such techniques have their limitations. For safety/security critical applications, more relevant measures of reliability and risk should be estimated. Moreover, ROC techniques do not explicitly account for: 1) inherent uncertainties one faces during assessments, 2) reliability evidence other than the observed failure behaviour of the classifier, and 3) how this observed failure behaviour alters one's uncertainty about classifier reliability. We address these limitations using conservative Bayesian inference (CBI) methods, producing statistically principled, conservative values for risk/reliability measures of interest. Our analyses reveals trade-offs amongst all binary classifiers with the same expected loss { the most reliable classifiers are those most likely to experience high impact failures. This trade-off is harnessed by using diverse redundant binary classifiers