The classification task for a real world application shall include a confidence estimation to handle unseen patterns i.e., patterns which were not considered during the learning stage of a classifier. This is important especially for safety critical applications where the goal is to assign these situations as ”unknown” before they can lead to a false classification. Several methods were propose...

Site-specific wind field characteristics have a significant impact on the structural response and the lifespan of wind turbines. This paper presents a machine learning approach towards analyzing and predicting the response of wind turbine structures to varying wind field characteristics. Machine learning algorithms are applied (i) to better understand changes of wind field characteristics due t...

Data are often collected from wild animals that have been marked at unknown age. As a result, standard probability models, fitted by maximum likelihood, cannot incorporate agedependence in probabilities of annual survival. We propose and fit new mixture models to ring-recovery data on birds ringed of unknown age, in which it is possible to incorporate age dependence in survival. It is shown tha...

Abundance estimates from animal point-count surveys require accurate estimates of detection probabilities. The standard model for estimating detection from removalsampled point-count surveys assumes that organisms at a survey site are detected at a constant rate; however, this assumption is often not justified. We consider a class of N-mixture models that allows for detection heterogeneity over...

The work is giving estimations of the discrepancy between solutions of the initial and the homogenized problems for a one-dimensional second-order elliptic operators with random coefficients satisfying strong or uniform mixing conditions. We obtain several sharp estimates in terms of the corresponding mixing coefficient.

—Reliable estimates of population parameters derived from survey methods are essential for decision making in management of endangered species. We evaluated whether point-count surveys used in conjunction with occupancy and binomial mixture models (BMMs) constituted a reliable approach for monitoring the federally endangered Golden-cheeked Warbler (Setophaga chrysoparia) on a preserve in centra...

The mixtures of experts (ME) model ooers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classiication problems the minimization of this ME error function leads to ME outputs estimating the...

Finite mixture models are statistical models which appear in many problems in statistics and machine learning. In such models it is assumed that data are drawn from random probability measures, called mixture components, which are themselves drawn from a probability measure P over probability measures. When estimating mixture models, it is common to make assumptions on the mixture components, s...

This paper describes a part of speech tagger. The tagger is based on a set of probability mixture models. Each mixture model is responsible for tagging of a specific class of words, sharing similar context properties. Probability mixture models contain 25 various mixture components. The tagger is tested on Polish language and compared to other available taggers.