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Boosting and Evolution
Mrnuštík, Michal ; Juránek, Roman (referee) ; Hradiš, Michal (advisor)
This thesis introduces combination of the AdaBoost and the evolutionary algorithm. The evolutionary algorithm is used to find linear combination of Haar features. This linear combination creates the feature to train weak classifier for AdaBoost. There are described basics of classification, Haar features and the AdaBoost. Next there are basic information about evolutionary algorithms. Theoretical description of combination of the AdaBoost and the evolutionary algorithm is included too. Some implementation details are added too. Implementation is tested on the images as part of the system for face recognition. Results are compared with Haar features.
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C#-JavaScript Transpiler for DotVVM
Mrnuštík, Michal ; Ryšavý, Ondřej (referee) ; Pluskal, Jan (advisor)
Aim of this thesis is to solve issue of transpiling server code written in C# to client-side JavaScript code, so it could be run in the browser. And then implementing the proposed solution. Next goal is to integrate this solution into framework DotVVM and develop an application demonstrating this techonology.
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Evolutionary Design Using Random Boolean Networks
Mrnuštík, Michal ; Žaloudek, Luděk (referee) ; Bidlo, Michal (advisor)
This master's thesis introduces the Random Boolean Networks as a developmental model in the evolutionary design. The representation of the Random Boolean Networks is described. This representation is combined with an evolutionary algorithm. The genetic operators are described too. The Random Boolean Networks are used as the developmental model for the evolutionary design of the combinational circuits and the sorting networks. Moreover a representation of the Random Boolean Networks for the design of image filters is introduced. The proposed methods are evaluated in different case-studies. The results of the experiments are discussed together with the potential improvements and topics of the next research.
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Machine-Learning Methods in Natural Language Processing
Vantuch, Marek ; Mrnuštík, Michal (referee) ; Otrusina, Lubomír (advisor)
Firstly, basic rules of tagging of the Czech language are described as well as problems connected to this field. Thereafter the focus of the thesis is put on the success rate of testing on the Czech corpus and at the same time trying to find the most suitable parameter values for using the features. After reaching a reasonable compromise between duration and accuracy, the value is then attempted to be improved using analysis of separate features and their eventual omission.
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Evolutionary Design of Simulator Based on Cellular Automata
Brigant, Vladimír ; Šperka, Svatopluk (referee) ; Mrnuštík, Michal (advisor)
This work describes concept of a cellular automata (CA) simulator, which is able to predict behaviour of a complex spatial system. This prediction is based on available training data and transition rule acquired from regression analysis powered by evolutionary algorithms. Two regression analysis methods (linear and logistic regression) are suggested, implemented and compared on urban growth prediction of Brno city.
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C#-JavaScript Transpiler for DotVVM
Mrnuštík, Michal ; Ryšavý, Ondřej (referee) ; Pluskal, Jan (advisor)
Aim of this thesis is to solve issue of transpiling server code written in C# to client-side JavaScript code, so it could be run in the browser. And then implementing the proposed solution. Next goal is to integrate this solution into framework DotVVM and develop an application demonstrating this techonology.
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Machine-Learning Methods in Natural Language Processing
Vantuch, Marek ; Mrnuštík, Michal (referee) ; Otrusina, Lubomír (advisor)
Firstly, basic rules of tagging of the Czech language are described as well as problems connected to this field. Thereafter the focus of the thesis is put on the success rate of testing on the Czech corpus and at the same time trying to find the most suitable parameter values for using the features. After reaching a reasonable compromise between duration and accuracy, the value is then attempted to be improved using analysis of separate features and their eventual omission.
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