Search results for "computer.software_genre"
showing 10 items of 3858 documents
A Feature Set Decomposition Method for the Construction of Multi-classifier Systems Trained with High-Dimensional Data
2013
Data mining for the discovery of novel, useful patterns, encounters obstacles when dealing with high-dimensional datasets, which have been documented as the "curse" of dimensionality. A strategy to deal with this issue is the decomposition of the input feature set to build a multi-classifier system. Standalone decomposition methods are rare and generally based on random selection. We propose a decomposition method which uses information theory tools to arrange input features into uncorrelated and relevant subsets. Experimental results show how this approach significantly outperforms three baseline decomposition methods, in terms of classification accuracy.
Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs
2014
We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation steps an additional penalty is set on top of the lasso penalty, similarly to Li and Li (2008). We develop a fast implementation for the new method based on coordinate descent. Furthermore, we show how the new methods can be applied to time-to-event data. The new method yields good results in simulation studies concerning sensitivity and specificity of non-zero covariate coefficients, estimation of networ…
Incrementally Assessing Cluster Tendencies with a~Maximum Variance Cluster Algorithm
2003
A straightforward and efficient way to discover clustering tendencies in data using a recently proposed Maximum Variance Clustering algorithm is proposed. The approach shares the benefits of the plain clustering algorithm with regard to other approaches for clustering. Experiments using both synthetic and real data have been performed in order to evaluate the differences between the proposed methodology and the plain use of the Maximum Variance algorithm. According to the results obtained, the proposal constitutes an efficient and accurate alternative.
Bayesian versus data driven model selection for microarray data
2014
Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…
Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality
2015
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…
Impact of temperature on obstructive sleep apnoea in three different climate zones of Europe: Data from the European Sleep Apnoea Database (ESADA)
2021
Recent studies indicate that ambient temperature may modulate obstructive sleep apnoea (OSA) severity. However, study results are contradictory warranting more investigation in this field. We analysed 19,293 patients of the European Sleep Apnoea Database (ESADA) cohort with restriction to the three predominant climate zones according to the Koppen-Geiger climate classification: Cfb (warm temperature, fully humid, warm summer), Csa (warm temperature, summer dry, hot summer), and Dfb (snow, fully humid, warm summer). Average outside temperature values were obtained and several hierarchical regression analyses were performed to investigate the impact of temperature on the apnea-hypopnea index …
Active spike transmission in the neuron model with a winding threshold manifold
2012
International audience; We analyze spiking responses of excitable neuron model with a winding threshold manifold on a pulse stimulation. The model is stimulated with external pulse stimuli and can generate nonlinear integrate-and-fire and resonant responses typical for excitable neuronal cells (all-or-none). In addition we show that for certain parameter range there is a possibility to trigger a spiking sequence with a finite number of spikes (a spiking message) in the response on a short stimulus pulse. So active transformation of N incoming pulses to M (with M>N) outgoing spikes is possible. At the level of single neuron computations such property can provide an active "spike source" comp…
Contextual neural-network based spectrum prediction for cognitive radio
2015
Cognitive radio is the technique of effective electromagnetic spectrum usage important for future wireless communication including 5G networks. Neural networks are nature-inspired computational models used to solve cognitive radio prediction problems. This paper presents the use of contextual Sigma-if neural network in prediction of channel states for cognitive radio. Our results indicate that Sigma-if neural network confirms better predictions than Multilayer Perceptron (MLP) network and decreases sensing time for the benefit of the increase of the effectiveness of e-m spectrum usage.
Analysis of the written handover process during shift changes within the hospital
2008
This study assesses the ergonomic quality of a new writing format used for the written transmission of activity during a nurse’s handover in a hospital. This format called “targeted or focussed transmission” comes from a new prescription of hospital management designed to improve the written handover process. Our research focused on the information filtering process for each patient concerned by a shift handover. A three step methodology was designed, with the participation of 9 nurses in charge of 70 patients : (1) nursing work analysis before the handover, (2) oral handover analysis, (3) written handover analysis. Results show that the new writing format does not match the nurses’ needs t…
Studying Utility of Personal Usage-History: A Software Tool for Enabling Empirical Research
2007
Managing personal information space and working context is complicated in computerized environment. One well-known cause for the problem is that digital information is superficially fragmented into different data types and structures. Several unifying approaches have been proposed to facilitate semantic connections between them. Particularly in personal information retrieval, temporal information has turned to be useful. Hence, in this article, we present an empirical research setting for studying the utility of representing personal usage-history in information retrieval by comparing it with more traditional hierarchical representation. The research setting is based on a software Tool that…