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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Gradient Boosting Machine and Artificial Neural Networks in R and H2O
 Sabo, Juraj ; Bašta, Milan (advisor) ; Plašil, Miroslav (referee)
Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.
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Analysis of time series of greenhouse gases emissions in the EU 27 during the period from 1990 to 2011
 Sabo, Juraj ; Helman, Karel (advisor) ; Kladívko, Kamil (referee)
Global climate change is one of the most serious environmental issues. According to current scientific knowledge, the global climate change is caused by the production of greenhouse gases. In response to this ongoing climate change the Kyoto Protocol to the United Nations Framework Agreement on Climate Change was adopted. This bachelor thesis is focused on the analysis of time series of total greenhouse gases emissions in the EU 27 during the period from 1990 to 2011. The aim is to obtain information about the behavior of the analyzed time series in the reporting period, verify the objectives of the Kyoto Protocol of reducing emitted greenhouse gases and forecast the future development of greenhouse gases emissions in EU 27. All data was obtained from public databases of Eurostat. Tools for achieving the objectives of the thesis are basic characteristics of time series and methods for modelling trend component through the selected trend functions and adaptive methods. This thesis consists of two main parts. The theoretical part is devoted to the description of the statistical methods and practical part is dedicated to the analysis of time series.
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See also: similar author names
5 Sabo, Jakub
10 Sabo, Jozef
2 Sabó, Ján
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