Difference: Papers20100616125928 (1 vs. 4)

Revision 42010-08-13 - TheresiaFreska

 
META TOPICPARENT name="AI_GEOSTATSPapers"
Title: Parameter Estimation in Neural Spatial Interaction Modelling by a Derivative Free Global Optimization Method

Date: 25 July 1999

Authors: Manfred M. Fischer, Martin Reismann, Katerina Hlavácková-Schindler

Link: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm

Abstract:

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient-based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows escape from local minima. Differential evolution recently has been introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behaviour in real-world applications. This paper explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against back propagation of conjugate gradients is based on Austrian interregional telecommunication traffic data.

Reference:

IV International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.

Deleted:
<
<
-- TWikiAdminUser - 2010-06-16

Revision 32010-07-24 - TWikiAdminUser

 
META TOPICPARENT name="AI_GEOSTATSPapers"
Title: Parameter Estimation in Neural Spatial Interaction Modelling by a Derivative Free Global Optimization Method
Changed:
<
<
Date:
>
>
Date: 25 July 1999
 
Changed:
<
<
Authors: Manfred M. Fischer, Martin Reismann, Katerina Hlav?ckov?-Schindler
>
>
Authors: Manfred M. Fischer, Martin Reismann, Katerina Hlavácková-Schindler
 
Changed:
<
<
Link: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm
>
>
Link: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm
  Abstract:

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient-based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows escape from local minima. Differential evolution recently has been introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behaviour in real-world applications. This paper explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against back propagation of conjugate gradients is based on Austrian interregional telecommunication traffic data.

Reference:

Changed:
<
<
IV International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.
>
>
IV International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.
  -- TWikiAdminUser - 2010-06-16

Revision 22010-07-23 - TWikiAdminUser

Changed:
<
<
META TOPICPARENT name="GeostatisticsPapers"
>
>
META TOPICPARENT name="AI_GEOSTATSPapers"
 Title: Parameter Estimation in Neural Spatial Interaction Modelling by a Derivative Free Global Optimization Method

Date:

Authors: Manfred M. Fischer, Martin Reismann, Katerina Hlav?ckov?-Schindler

Link: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm

Abstract:

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient-based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows escape from local minima. Differential evolution recently has been introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behaviour in real-world applications. This paper explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against back propagation of conjugate gradients is based on Austrian interregional telecommunication traffic data.

Reference:

IV International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.

-- TWikiAdminUser - 2010-06-16

Revision 12010-06-16 - TWikiAdminUser

 
META TOPICPARENT name="GeostatisticsPapers"
Title: Parameter Estimation in Neural Spatial Interaction Modelling by a Derivative Free Global Optimization Method

Date:

Authors: Manfred M. Fischer, Martin Reismann, Katerina Hlav?ckov?-Schindler

Link: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm

Abstract:

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient-based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows escape from local minima. Differential evolution recently has been introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behaviour in real-world applications. This paper explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against back propagation of conjugate gradients is based on Austrian interregional telecommunication traffic data.

Reference:

IV International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.

-- TWikiAdminUser - 2010-06-16

 
This site is powered by the TWiki collaboration platformCopyright © by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding TWiki? Send feedback