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Vytvoření predikčního modelu předpovědi počasí pomocí neuronové sítě a asociačních pravidel

By Jakub Kadlec


This diploma thesis introduces three different methods of creating a neural network binary classifier for the purpose of automated weather prediction with attribute pre-selection using association rules and correlation patters mining by the LISp-Miner system. First part of the thesis consists of collection of theoretical knowledge enabling the creation of such predictive model, whereas the second part describes the creation of the model itself using the CRISP-DM methodology. Final part of the thesis analyses the performance of created classifiers and concludes the proposed methods and their possible benefits over training the network without attribute pre-selection

Topics: korelace; neural networks; KL-miner; 4ft-miner; correlation; neuronové sítě; předpověď počasí; asociační pravidla; weather prediction; association rules; 4ft-Miner; KL-Miner
Publisher: Vysoká škola ekonomická v Praze
Year: 2016
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