scholarly journals Predictive modeling techniques with application to the Cerulean Warbler (Dendroica cerulea) in the Appalachian Mountains Bird Conservation Region

2009 ◽  
Author(s):  
Matthew Buhrl Shumar
1992 ◽  
Vol 18 (11) ◽  
pp. 979-987 ◽  
Author(s):  
T.M. Khoshgoftaar ◽  
J.C. Munson ◽  
B.B. Bhattacharya ◽  
G.D. Richardson

2013 ◽  
Vol 4 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Thomas A. Woolman ◽  
John C. Yi

This study addresses the use of predictive modeling techniques; primarily feed-forward artificial neural networks as a tool for forecasting geological exploration targets for gold prospecting. It also provides evidence of effectiveness of using Business Intelligence systems to model pathfinder variables, anomaly detection, and forecasting to locate potential exploration sites for precious metals. The results indicate that the use of advanced Business Intelligence systems can be of extremely high value to the extractive minerals exploration industry.


Author(s):  
Claudia Perlich ◽  
Foster Provost

Most data mining and modeling techniques have been developed for data represented as a single table, where every row is a feature vector that captures the characteristics of an observation. However, data in most domains are not of this form and consist of multiple tables with several types of entities. Such relational data are ubiquitous; both because of the large number of multi-table relational databases kept by businesses and government organizations, and because of the natural, linked nature of people, organizations, computers, and etc. Relational data pose new challenges for modeling and data mining, including the exploration of related entities and the aggregation of information from multi-sets (“bags”) of related entities.


1999 ◽  
Author(s):  
Donis G. Flagello ◽  
Hans van der Laan ◽  
Jan B. van Schoot ◽  
Igor Bouchoms ◽  
Bernd Geh

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