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Document Classification
Marek, Tomáš ; Škoda, Petr (referee) ; Otrusina, Lubomír (advisor)
This thesis deals with a document classification, especially with a text classification method. Main goal of this thesis is to analyze two arbitrary document classification algorithms to describe them and to create an implementation of those algorithms. Chosen algorithms are Bayes classifier and classifier based on support vector machines (SVM) which were analyzed and implemented in the practical part of this thesis. One of the main goals of this thesis is to create and choose optimal text features, which are describing the input text best and thus lead to the best classification results. At the end of this thesis there is a bunch of tests showing comparison of efficiency of the chosen classifiers under various conditions.
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Document Classification
Marek, Tomáš ; Škoda, Petr (referee) ; Otrusina, Lubomír (advisor)
This thesis deals with a document classification, especially with a text classification method. Main goal of this thesis is to analyze two arbitrary document classification algorithms to describe them and to create an implementation of those algorithms. Chosen algorithms are Bayes classifier and classifier based on support vector machines (SVM) which were analyzed and implemented in the practical part of this thesis. One of the main goals of this thesis is to create and choose optimal text features, which are describing the input text best and thus lead to the best classification results. At the end of this thesis there is a bunch of tests showing comparison of efficiency of the chosen classifiers under various conditions.
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Meta-Parameters of Kernel Methods and Their Optimization
Vidnerová, Petra ; Neruda, Roman
In this work we deal with the problem of metalearning for kernel based methods. Among the kernel methods we focus on the support vector machine (SVM), that have become a method of choice in a wide range of practical applications, and on the regularization network (RN) with a sound background in approximation theory. We discuss the role of kernel function in learning, and we explain several search methods for kernel function optimization, including grid search, genetic search and simulated annealing. The proposed methodology is demonstrated on experiments using benchmark data sets.
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Jádrové regularizční sítě
Kudová, Petra
The paper deals with Kernel Based Regularization Networks. We propose a technique for estimation of the explicit parameters of the learning algorithm and introduce new types of kernels - Product and Sum Kernels. Described algorithms are demonstrated on experiments.
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