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Multi-Label Classification of Text Documents
Průša, Petr ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.
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Multiple Type Office Documents Diff
Varga, Tomáš ; Volf, Tomáš (referee) ; Chmelař, Petr (advisor)
This bachelor thesis deals with comparing different types of office documents. The aim of this thesis is to create a module into an existing application called mediadiff for comparing documents of the Open Office office suite, including text documents, presentations and spreadsheets. This thesis describes algorithms for comparing text and tree structures. Furthermore, there are described the best known office suites and Access to the content of their documents.
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Multi-Label Classification of Text Documents
Průša, Petr ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.
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Multiple Type Office Documents Diff
Varga, Tomáš ; Volf, Tomáš (referee) ; Chmelař, Petr (advisor)
This bachelor thesis deals with comparing different types of office documents. The aim of this thesis is to create a module into an existing application called mediadiff for comparing documents of the Open Office office suite, including text documents, presentations and spreadsheets. This thesis describes algorithms for comparing text and tree structures. Furthermore, there are described the best known office suites and Access to the content of their documents.
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Klasifikace textových dokumentů
Humpolíček, Jiří
In this report, we propose four feature selection algorithms based on the Best Individual Feature method and one based on the sequential method. After that the best method is selected for following classifier methods comparison. In this step we compare classification performance and computation expense of two classifiers based on Naive Bayes and third classifier is SVM. Classification performance is tested on the Reuters data set and Newsgroup data set. Finally we shows results on the multi-labelled subset of the Reuters data set.
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