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Utilization of artificial intelligence in vibrodiagnostics
Dočekalová, Petra ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis deals with machine learning, expert systems, fuzzy logic, genetic algorithms, neural networks and chaos theory, which fall into the category of artificial intelligence. The aim of this work is to describe and implement three different classification methods, according to which the data set will be processed. The GNU Octave software environment was chosen for the data application for licensing reasons. Further evaluate the success of data classification, including visualization. Three different classification methods are used for comparison, so that we can compare the processed data with each other.
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Methods for Classification of WWW Pages
Svoboda, Pavel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.
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Statistical Classification Methods
Barvenčík, Oldřich ; Žák, Libor (referee) ; Michálek, Jaroslav (advisor)
The thesis deals with selected classification methods. The thesis describes the basis of cluster analysis, discriminant analysis and theory of classification trees. The usage is demonstrated by classification of simulated data, the calculation is made in the program STATISTICA. In practical part of the thesis there is the comparison of the methods for classification of real data files of various extent. Classification methods are used for solving of the real task – prediction of air pollution based of the weather forecast.
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Rozpoznávání drah částic v pixelovém detektoru typu Timepix
Čermák, Jakub ; Čermák, Pavel (advisor) ; Doležal, Zdeněk (referee)
In current particle physics field, the progressive detection technologies are used. The pixel detectors are one of them. These detectors are divided into small subdetectors (pixels), which allow viewing exact tracks of the detected particles. This thesis defines criteria for mathematical description of the shape of the particle tracks of different kinds (e-, γ, p, α, μ) and compares several methods used for a classification -neural networks, decision trees and others. The Pixa software was implemented to process the data measured by pixel detectors. This software implements the characteristics and classification methods and creates statistical and other physical results.
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Utilization of artificial intelligence in vibrodiagnostics
Dočekalová, Petra ; Huzlík, Rostislav (referee) ; Zuth, Daniel (advisor)
The diploma thesis deals with machine learning, expert systems, fuzzy logic, genetic algorithms, neural networks and chaos theory, which fall into the category of artificial intelligence. The aim of this work is to describe and implement three different classification methods, according to which the data set will be processed. The GNU Octave software environment was chosen for the data application for licensing reasons. Further evaluate the success of data classification, including visualization. Three different classification methods are used for comparison, so that we can compare the processed data with each other.
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Consumer Credit Risk Analysis: Evidence from the Czech Republic
Mittigová, Patricie ; Kočenda, Evžen (advisor) ; Hlaváček, Michal (referee)
An increase in the number of granted loans in last decades resulted in more attention paid to proper assessment of borrower's creditworthiness. For this purpose, credit scoring aims to classify good and bad applicants prior loan granting. In this thesis, I analyze a large real-world dataset of borrowers who were granted an unsecured consumer loan in the Czech Republic. The objec- tive is to determine core default predictors while employing seven classification methods. Additionally, a performance measure is computed for each method in order to compare their suitability for examined loan types. Using logistic regression as the core model, the results suggest that borrower's age, monthly income, region of residence, and the number of children substantially influence the probability of default. Conversely, borrower's gender and education level did not prove to be significant for assessing client's creditworthiness. Compar- ing the performance of employed classification methods, it can be concluded that all models produced almost identical results and can be used for the purpose of credit scoring. This thesis complements rather a limited number of credit scoring studies in the Czech Republic and provides new findings about default determinants for unsecured consumer loans. 1
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Rozpoznávání drah částic v pixelovém detektoru typu Timepix
Čermák, Jakub ; Čermák, Pavel (advisor) ; Doležal, Zdeněk (referee)
In current particle physics field, the progressive detection technologies are used. The pixel detectors are one of them. These detectors are divided into small subdetectors (pixels), which allow viewing exact tracks of the detected particles. This thesis defines criteria for mathematical description of the shape of the particle tracks of different kinds (e-, γ, p, α, μ) and compares several methods used for a classification -neural networks, decision trees and others. The Pixa software was implemented to process the data measured by pixel detectors. This software implements the characteristics and classification methods and creates statistical and other physical results.
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Data Classification Methods
Kaščák, Pavol ; Šebek, Michal (referee) ; Bartík, Vladimír (advisor)
This bachelor's thesis deals with data classification, focusing on the implementation of naive Bayes classification method. At First, it is generally described process of data classification, its division into phases with their characteristic. It is followed by a more accurate description of the naive Bayes classification method and description of the implementation by using Java programming language and MySQL database. The last section contains a summary of the results.
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Representation of Text and Its Influence on Categorization
Šabatka, Ondřej ; Chmelař, Petr (referee) ; Bartík, Vladimír (advisor)
The thesis deals with machine processing of textual data. In the theoretical part, issues related to natural language processing are described and different ways of pre-processing and representation of text are also introduced. The thesis also focuses on the usage of N-grams as features for document representation and describes some algorithms used for their extraction. The next part includes an outline of classification methods used. In the practical part, an application for pre-processing and creation of different textual data representations is suggested and implemented. Within the experiments made, the influence of these representations on accuracy of classification algorithms is analysed.
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Methods for Classification of WWW Pages
Svoboda, Pavel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The main goal of this master's thesis was to study the main principles of classification methods. Basic principles of knowledge discovery process, data mining and using an external class CSSBox are described. Special attantion was paid to implementation of a ,,k-nearest neighbors`` classification method. The first objective of this work was to create training and testing data described by 'n' attributes. The second objective was to perform experimental analysis to determine a good value for 'k', the number of neighbors.
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