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Web Application for Managing and Classifying Information from Distributed Sources
Vrána, Pavel ; Chmelař, Petr (referee) ; Drozd, Michal (advisor)
This master's thesis deals with data mining techniques and classification of the data into specified categories. The goal of this thesis is to implement a web portal for administration and classification of data from distributed sources. To achieve the goal, it is necessary to test different methods and find the most appropriate one for web articles classification. From the results obtained, there will be developed an automated application for downloading and classification of data from different sources, which would ultimately be able to substitute a user, who would process all the tasks manually.
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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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Acoustic signal classification
Pospíšil, Aleš ; Balík, Miroslav (referee) ; Atassi, Hicham (advisor)
Bachelor's thesis is focused on automatic music genre classication. First part of work evaluates present situation and refer to published studies. Gained knowledge from there is applied in this work. In terms of nding solution for problem the work summarize and describe suitable music features and classication techniques like neural networks and k-nearest neighbor. Four selected classication classes were classical, electro, jazz and rock music. Result of work is user-friendly system that provides automatic music genre recognition. Achieved classication performance is more less comparable to human music genres recognition.
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Influence of Metric on Classification Error of Distance-Based Classifiers
Jiřina, Marcel
Five types of classifiers that use sample distances for class estimation of an unknown sample was tested. Each classifier was tested with fifteen different metrics on 24 classification tasks from the UCI Machine Learning Repository. The metrics were compared and the best of them was found for each classifier. Surprisingly, the best metrics for all five types of classifiers is the Hassanat metrics. Classifiers were also compared and ranked according to their classification ability. Wilcoxon Test and Friedman Aligned test were used for statistical evaluation.
Fulltext: content.csg -  PDF
Plný tet: v1211-14 - PDF
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