There has been an enormous amount of resources spent on collecting data related to construction accidents but there are very few researches done on analyzing the collected data beyond trend analysis. This research is based on the premise that construction accident reports can be exploited much more than they currently are to obtain valuable information. Analyzing texts in accident reports in ad...

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable m...

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is ...

In many scientific fields, models are used to characterize relationships and processes, as well as to predict outcomes from initial conditions and inputs. These models can support the decision-making process by allowing investigators to consider the likely effects of possible interventions and identify efficient ways to achieve desired outcomes. Machine learning research on constructing complex...

soil texture is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. detailed information on soil texture variability is crucial for proper crop and land management and environmental studies. therefore, at present research, 103 soil profiles were dogged and then sampled in order to prepare digital map of soil texture in bijar...

abstract: despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. therefore, in most of these research the predictive variables were chosen randomly and accordin...

Predictive Models for Point Processes by Wenzhao Lian Department of Statistical Science Duke University Date: Approved: David Dunson, Supervisor

In this paper we investigate predictive models of form filling. A predictive model of form filling aims to reduce the amount of time a user spends filling out a form by predicting the values of fields on the form and using these predictions to make suggestions to the form filler. Existing predictive models ignore both the values of other fields on the form and the values previously entered by u...

Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the concept shifts may not have to satisfy any further requirements and may occur innnitely often. Within this work is studied to what extent it is still possible to predict or learn values for a data sequence produced by dri...

The ECML-PKDD 2006 Discovery Challenge posed a topical problem for predictive modelers: how to separate SPAM from non-SPAM email using classic word count descriptions of email messages. The data for the challenge were released around March 1, 2006 and submissions were due June 7, 2006, allowing entrants to devote as much as three months to preparing and modeling the data. We devoted two calenda...