By Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)
This e-book constitutes the refereed lawsuits of the ninth commercial convention on facts Mining, ICDM 2009, held in Leipzig, Germany in July 2009.
The 32 revised complete papers provided have been rigorously reviewed and chosen from one hundred thirty submissions. The papers are equipped in topical sections on information mining in medication and agriculture, info mining in advertising, finance and telecommunication, info mining in technique keep watch over, and society, facts mining on multimedia information and theoretical facets of knowledge mining.
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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings
Proceedings of IPMU 2008, pp. 576–582. University of M´alaga (June 2008) 26. : Data mining with neural networks for wheat yield prediction. In: Perner, P. ) ICDM 2008. LNCS, vol. 5077, pp. 47–56. Springer, Heidelberg (2008) 27. : Prerequisites for the adoption of new technologies - the example of precision agriculture. In: Agricultural Engineering for a Better World, D¨usseldorf. VDI Verlag GmbH (2006) 28. : Corn yield prediction with artificial neural network trained using airborne remote sensing and topographic data.
20 J. Chilo et al. Fig. 3. Overfitting shown with program size and number of correctly identified sam- ples in the training and testing data set If a small increase in model complexity gives a big increase in classification accuracy, we typically have a change in the model without any overfitting. In other words, if a small amount of extra theory can explain many more observations, that extra theory is believed to be generally valid. As can be seen in Fig. 3, there is an equally big jump in accuracy for both training and testing data when moving to the simple rule above, which has a size of about 34 bits according to ADATE’s built in syntactic complexity measure.
Radial basis function (RBF) networks are similar to multi-layer perceptrons in that they can also be used to model non-linear relationships between input data. Nevertheless, there has been almost no research into RBF networks when applying them to agriculture data. 2. Data Mining of Agricultural Yield Data: A Comparison of Regression Models 29 Regression trees have seen some usage in agriculture [6,12,14]. Essentially, they are a special case of decision trees where the outcome (in the tree leaves) is a continuous function instead of a discrete classification.
Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings by Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)