By Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)
This e-book constitutes the refereed lawsuits of the ninth business convention on information Mining, ICDM 2009, held in Leipzig, Germany in July 2009.
The 32 revised complete papers awarded have been rigorously reviewed and chosen from a hundred thirty submissions. The papers are geared up in topical sections on facts mining in drugs and agriculture, facts mining in advertising and marketing, finance and telecommunication, info mining in strategy regulate, and society, information mining on multimedia info and theoretical features of information mining.
Read or Download Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings PDF
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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
Three additional modeling techniques will be presented that are suitable for the task of yield prediction. In the past, numerous regression techniques have been used successfully on data from agriculture. Neural networks have shown to be quite effective in modeling yield of different crops ([7,28]). In  and , artificial neural networks, namely multilayer perceptrons (MLPs) have been trained to predict wheat yield from fertilizer and additional sensor input. 2. 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.
Furthermore, from the agricultural perspective, it is interesting to see how much the factor “fertilization” influences the yield in the current site-year. For this purpose, modeling techniques can be used, but have to be evaluated first. Therefore, this work aims at finding suitable data models that achieve a high accuracy and a high generality in terms of yield prediction capabilities. For this purpose, different types of regression techniques will be evaluated on different data sets. Since models usually are strongly parameterized, an additional question is whether the model parameters can be carried over from one field to other fields which are comparable in (data set) size.
Fast Effective Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123 (1995) 10. : Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. In: NIPS conference, pp. 557–563 (1999) 11. 5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993) Electronic Nose Ovarian Carcinoma Diagnosis 23 12. : Inductive functional programming using incremental program transformation. Artificial Intelligence 1, 55–83 (1995) 13.
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.)