We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. Previ­ ous work has concentrated on metrics for do­ mains containing only discrete variables, un­ der the assumption that data represents a multinomial sample. In this paper, we ex­ tend this work, developing scoring metrics for domains containing only continuous variables under...

Flight delay creates major problems in the current aviation system. Methods are needed to analyze the manner in which micro-level causes propagate to create system-level patterns of delay. Traditional statistical methods are inadequate to the task. This paper proposes the use of Bayesian networks (BNs) to investigate and visualize propagation of delays among airports. The BN structure was devel...

We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner. Our method combines existing BNP clustering and discriminative models by enforcing latent cluster indices to be consistent with the predicted labels resulted from probabilistic discriminative model. Thi...

Automatic classification of human personality along the Big Five dimensions is an interesting problem with several practical applications. This paper makes some contributions in this regard. First, we propose a few automatically-derived personality-discriminating lexical features which provide information complementary to the conventional acoustic-prosodic cues. We also design a frame-level Gau...

The effectiveness of interventions to improve pedestrian safety, whether involving engineering, education, or enforcement, is limited by the behavior of the public in response to the interventions. It is not difficult to imagine that the behavioral response of an individual to engineering and educational road safety interventions may be at least partially explained by demographic characteristic...

Web stores, where buyers place orders over the Internet, have emerged to become a prevalent sales channel. In this research, we developed neural network models, which are known for their capability of modeling noncompensatory decision processes, to predict and explain consumer choice between web and traditional stores. We conducted an empirical survey for the study. Specifically, in the survey,...

Study objective: It is increasingly recognised that different dimensions of social inequality may be linked to health by different pathways. Furthermore, factors operating at the individual level such as employment conditions may affect health in a different way from household level factors. The paper examines the associations between self rated health and four measures of social position— occu...

Logistic models are arguably one of the most widely used data analysis techniques. In this paper, we present analyses focussing on two important aspects of logistic models—its relationship with exponential family based generative models, and its performance in online and potentially adversarial settings. In particular, we present two new theoretical results on logistic models focusing on the ab...

This paper presents experimental comparison between selected neural architectures for chaotic time series prediction problem. Several feed-forward architectures (Multilayer Perceptrons) are compared with partially recurrent nets (Elman, extended Elman, and Jordan) based on convergence rate, prediction accuracy, training time requirements and stability of results. Results for chaotic logistic ma...

Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently...