Multimodal automatic user disposition recognition in human-machine interaction

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013

Citation-based Plagiarism Detection : applying citation pattern analysis to identify currently non-machine-detectible disguised plagiarism in scientific publications

Magdeburg, Univ., Fak. für Informatik, Diss., 2013

Three new snakes from the Peruvian Andes, by Karl P. Schmidt and Warren F. Walker.

v.24:no.28(1943)

Feather mites of the Aralichus canestrinii (Trouessart) complex (Acarina, Pterolichidae) from New World Parrots (Psittacidae) : I. From the genera Ara Lacépède and Anodorhynchus Spix / Warren T. Atyeo.

n.s. no.47(1988)

Snakes of the Peruvian coastal region, by Karl P. Schmidt and Warren F. Walker.

v.24:no.27(1943)

Peruvian snakes from the University of Arequipa, by Karl P. Schmidt and Warren F. Walker.

v.24:no.26(1943)

Feather mites of the Aralichus canestrinii (Trouessart) complex (Acarina, Pterolichidae) from New World parrots (Psittacidae). and conclusions to the study / Warren T. Atyeo, Tila M. Pérez.

n.s. no.62(1990)

┴N as an Abstract Elementary Class

"Vegeu el resum a l'inici del documents del fitxer adjunt"

Abstract splines in Krein spaces

"Vegeu el resum a l'inici del document del fitxer adjunt."

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

On absoluteness of categoricity in abstract elementary classes

"Vegeu el resum a l'inici del document del fitxer adjunt"

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.