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an experimental analysis using real world data

by Michele Bernasconi, Oliver Kirchkamp, Paolo Paruolo, Facoltà Di Economia, Copyright M. Bernasconi, O. Kirchkamp, P. Paruolo, Michele Bernasconi, Oliver Kirchkamp, Paolo Paruolo , 2003
"... Expectations and perceived causality in fiscal policy: an experimental analysis using real world data ..."
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Expectations and perceived causality in fiscal policy: an experimental analysis using real world data

Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem

by Mauricio A. Hernández, Salvatore J. Stolfo - DATA MINING AND KNOWLEDGE DISCOVERY , 1998
"... The problem of merging multiple databases of information about common entities is frequently encountered in KDD and decision support applications in large commercial and government organizations. The problem we study is often called the Merge/Purge problem and is difficult to solve both in scale and ..."
Abstract - Cited by 250 (0 self) - Add to MetaCart
and accuracy. Large repositories of data typically have numerous duplicate information entries about the same entities that are difficult to cull together without an intelligent "equational theory" that identifies equivalent items by a complex, domain-dependent matching process. We have developed a

Measuring Real World Data Availability

by Lisa Th Systems, Larry Lancaster, Alan Rowe - In Proceedings of the LISA 2001 15th Systems Administration Conference , 2001
"... Permission is granted for noncommercial reproduction of the work for educational or research purposes. ..."
Abstract - Cited by 14 (0 self) - Add to MetaCart
Permission is granted for noncommercial reproduction of the work for educational or research purposes.

Analyses of Real-World Data and Their Exploitation

by Jakub Stárka , 2013
"... ..."
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Abstract not found

Spotting Method for Classification of Real World Data

by unknown authors
"... This paper makes the case for a spotting computation scheme which gives rise to a new classification methodology for processing real world data by surveying algorithms developed under the Real World Computing (RWC) program and related work in Japan. A spotting function has the segmentation-free char ..."
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This paper makes the case for a spotting computation scheme which gives rise to a new classification methodology for processing real world data by surveying algorithms developed under the Real World Computing (RWC) program and related work in Japan. A spotting function has the segmentation

Spotting Method for Classification of Real World Data

by unknown authors
"... This paper makes the case for a spotting computation scheme which gives rise to a new classification methodology for processing real world data by surveying algorithms developed under the Real World Computing (RWC) program and related work in Japan. A spotting function has the segmentation-free char ..."
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This paper makes the case for a spotting computation scheme which gives rise to a new classification methodology for processing real world data by surveying algorithms developed under the Real World Computing (RWC) program and related work in Japan. A spotting function has the segmentation

Classifying real-world data with the DDα-procedure

by Pavlo Mozharovskyi, Karl Mosler, Tatjana Lange , 2013
"... The DDα-classifier, a nonparametric fast and very robust procedure, is de-scribed and applied to fifty classification problems regarding a broad spectrum of real-world data. The procedure first transforms the data from their original property space into a depth space, which is a low-dimensional unit ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
The DDα-classifier, a nonparametric fast and very robust procedure, is de-scribed and applied to fifty classification problems regarding a broad spectrum of real-world data. The procedure first transforms the data from their original property space into a depth space, which is a low

Video-based Face Recognition on Real-World Data

by Johannes Stallkamp, Hazım K. Ekenel
"... In this paper, we present the classification sub-system of a real-time video-based face identification system which recognizes people entering through the door of a laboratory. Since the subjects are not asked to cooperate with the system but are allowed to behave naturally, this application scenari ..."
Abstract - Cited by 36 (5 self) - Add to MetaCart
In this paper, we present the classification sub-system of a real-time video-based face identification system which recognizes people entering through the door of a laboratory. Since the subjects are not asked to cooperate with the system but are allowed to behave naturally, this application

Using Real-World Data for Coverage and Payment Decisions: The ISPOR Real-World DataTask Force Report

by Louis P. Garrison, Peter J. Neumann, Pennifer Erickson, Deborah Marshall, C. Daniel Mullins
"... Objectives: Health decision-makers involved with coverage and payment policies are increasingly developing policies that seek information on “real-world ” (RW) outcomes. Moti-vated by these initiatives, the International Society for Phar-macoeconomics and Outcomes Research (ISPOR) created a Task For ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Force on Real-World Data to develop a framework to assist health-care decision-makers in dealing with RW data, especially related to coverage and payment decisions. Methods: Task Force cochairs were selected by the ISPOR Board of Directors. Cochairs selected chairs for four working groups on: clinical

Automated Detection of Outliers in Real-World Data

by Mark Last, Abraham Kandel - Proc. of the Second International Conference on Intelligent Technologies , 2001
"... Most real-world databases include a certain amount of exceptional values, generally termed as "outliers". The isolation of outliers is important both for improving the quality of original data and for reducing the impact of outlying values in the process of knowledge discovery in databases ..."
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Most real-world databases include a certain amount of exceptional values, generally termed as "outliers". The isolation of outliers is important both for improving the quality of original data and for reducing the impact of outlying values in the process of knowledge discovery
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