Understanding systematic and random CD variations using predictive modeling techniques

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

Predictive modeling techniques of software quality from software measures

1992 ◽  
Vol 18 (11) ◽  
pp. 979-987 ◽  
Author(s):  
T.M. Khoshgoftaar ◽  
J.C. Munson ◽  
B.B. Bhattacharya ◽  
G.D. Richardson
Keyword(s):  
Predictive Modeling ◽  
Software Quality ◽  
Modeling Techniques ◽  
Software Measures

An Analysis of the Use of Predictive Modeling with Business Intelligence Systems for Exploration of Precious Metals Using Biogeochemical Data

2013 ◽  
Vol 4 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Thomas A. Woolman ◽  
John C. Yi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Anomaly Detection ◽  
Predictive Modeling ◽  
Business Intelligence ◽  
Precious Metals ◽  
Geological Exploration ◽  
Feed Forward ◽  
Business Intelligence Systems ◽  
Modeling Techniques

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.

Aggregation for Predictive Modeling with Relational Data

2011 ◽  
pp. 33-38
Author(s):  
Claudia Perlich ◽  
Foster Provost
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Relational Databases ◽  
Feature Vector ◽  
Relational Data ◽  
Government Organizations ◽  
Modeling Techniques ◽  
Aggregation Of Information ◽  
New Challenges

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.

The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands

2003 ◽  
Vol 34 (4) ◽  
pp. 471-481 ◽  
Author(s):  
Peter C. Verhoef ◽  
Penny N. Spring ◽  
Janny C. Hoekstra ◽  
Peter S.H. Leeflang
Keyword(s):  
The Netherlands ◽  
Predictive Modeling ◽  
Database Marketing ◽  
Commercial Use ◽  
Modeling Techniques

scholarly journals Predictive modeling techniques for nanosecond-laser damage growth in fused silica optics

2012 ◽  
Vol 20 (14) ◽  
pp. 15569 ◽  
Author(s):  
Zhi M. Liao ◽  
Ghaleb M. Abdulla ◽  
Raluca A. Negres ◽  
David A. Cross ◽  
Christopher W. Carr
Keyword(s):  
Predictive Modeling ◽  
Fused Silica ◽  
Laser Damage ◽  
Nanosecond Laser ◽  
Damage Growth ◽  
Modeling Techniques
Sign in / Sign up

Export Citation Format

Share Document