Difference: Papers20100623115423 (1 vs. 3)

Revision 32010-08-13 - TheresiaFreska

 
META TOPICPARENT name="AI_GEOSTATSPapers"
Title: Investigation of Two Neural Network Methods in an Automatic Mapping Exercise
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Date:
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Date: 1 July 2005
  Authors: Sridhar Dutta, Rajive Ganguli, Biswajit Samanta
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Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050020
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Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050020
  Abstract:

This paper investigates the performance of two neural network (NN) methods viz. a radial basis function network (RBFN) and a multilayer feed forward network (MFFN) to predict the radioactivity levels at a given test site. A comparative evaluation of the two networks is done using Root mean square error (RMSE), Pearson's r, Mean error (ME) and Mean Absolute error (MAE). It was found that the RBFN performed marginally better compared to the other method.

Reference:

Applied GIS
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050020

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-- TWikiAdminUser - 2010-06-16

Revision 22010-07-23 - TWikiAdminUser

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META TOPICPARENT name="GeostatisticsPapers"
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META TOPICPARENT name="AI_GEOSTATSPapers"
 Title: Investigation of Two Neural Network Methods in an Automatic Mapping Exercise

Date:

Authors: Sridhar Dutta, Rajive Ganguli, Biswajit Samanta

Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050020

Abstract:

This paper investigates the performance of two neural network (NN) methods viz. a radial basis function network (RBFN) and a multilayer feed forward network (MFFN) to predict the radioactivity levels at a given test site. A comparative evaluation of the two networks is done using Root mean square error (RMSE), Pearson's r, Mean error (ME) and Mean Absolute error (MAE). It was found that the RBFN performed marginally better compared to the other method.

Reference:

Applied GIS
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050020

-- TWikiAdminUser - 2010-06-16

Revision 12010-06-23 - TWikiAdminUser

 
META TOPICPARENT name="GeostatisticsPapers"
Title: Investigation of Two Neural Network Methods in an Automatic Mapping Exercise

Date:

Authors: Sridhar Dutta, Rajive Ganguli, Biswajit Samanta

Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050020

Abstract:

This paper investigates the performance of two neural network (NN) methods viz. a radial basis function network (RBFN) and a multilayer feed forward network (MFFN) to predict the radioactivity levels at a given test site. A comparative evaluation of the two networks is done using Root mean square error (RMSE), Pearson's r, Mean error (ME) and Mean Absolute error (MAE). It was found that the RBFN performed marginally better compared to the other method.

Reference:

Applied GIS
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050020

-- TWikiAdminUser - 2010-06-16

 
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