Difference: ConfIAED2009 (2 vs. 3)

Revision 32010-08-13 - TheresiaFreska

 
META TOPICPARENT name="AI_GEOSTATSConferences"

IAED 09

S4 ENVISA Workshop 2009

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http://www.lisut.org/workshop/iaed_09/
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http://www.lisut.org/workshop/iaed_09/ - link is broken, will be checked soon
 
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18-20 June 2009, University of Palermo, Palermo , Italy
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18-20 June 2009
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University of Palermo, Palermo , Italy
  Workshop Description

Large databases and long periods of environmental observation, monitoring of pollution, rare and extreme events and recent remote sensing technologies demand new analytical and processing tools. These tools can be provided by Machine Learning (ML) , which is a general and powerful field in data processing and modeling, and Exploratory Data Analysis (EDA) , which is an approach to discover underlying features contained in data. The first two days of the workshop will provide the cutting-edge data analysis and modeling tools by presenting concepts, algorithms, and real case studies from environmental problems, natural hazards, natural and renewable resources, socio-economic data and other fields of application. It consists of a series of seminars and presentations on geo- and environmental data analysis, treatment and visualization using intelligent modelling techniques based on Machine Learning Algorithms and not only. The last day will offer to the participants the possibility to work on their data by applying ML algorithms and using specific software. The workshop will be useful both for the beginners and advanced researchers and users.

Workshop Themes

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  • Environmental data mining
  • Learning from geospatial data
  • MLA and geostatistics
  • Artificial neural networks: multilayer perception, general regression neural networks, probabilistic neural networks, radial basis function networks, self-organising maps
  • Statistical learning theory: support vector machines, kernel methods
  • Remote sensing: enhanced technique for remote sensed images treatment
  • Case studies: spatial classification, spatial prediction, density modeling, monitoring network optimisation, novelty detection
 
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