In this paper we present a visual learning approach that uses non-parametric probability estimators. We use entropy analysis over the training set in order to select the features that best represent the pattern class of faces, and set up discrete probability models. These models are tested in the context of maximum likelihooddetection of faces. Excellent results are reported in terms of the cor...

• In this work, an optimal alarm system is developed to predict whether a financial time series modeled via Fractionally Integrated Asymmetric Power ARCH (FIAPARCH) models, up/downcrosses some particular level and give an alarm whenever this crossing is predicted. The paper presents classical and Bayesian methodology for producing optimal alarm systems. Both methodologies are illustrated and th...

Learning to predict rare events from sequences of events with categorical features is an important, real-world, problem that existing statistical and machine learning methods are not well suited to solve. This paper describes timeweaver, a genetic algorithm based machine learning system that predicts rare events by identifying predictive temporal and sequential patterns. Timeweaver is applied t...

27 Social pollinators such as honey bees face attacks from predators not only at the nest, but also during 28 foraging. Pollinating honey bees can therefore release alarm pheromones that deter conspecifics from visiting 29 dangerous inflorescences. However, the effect of alarm pheromone and its chemical components upon bee 30 avoidance of dangerous food sources remains unclear. We tested the re...

A constant false alarm rate (CFAR) algorithm has been developed for use in multi-band mine detection. While it is often difficult to predict the spectral signatures of targets, the shape of the target may be known. This test exploits geometric target features and spectral differences between the target and the surrounding area. The algorithm is derived from a general statistical model of the da...