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Machine Learning Concepts for Categorization of Objects in Images
Hubený, Marek ; Honec, Peter (referee) ; Horák, Karel (advisor)
This work is focused on objects and scenes recognition using machine learning and computer vision tools. Before the solution of this problem has been studied basic phases of the machine learning concept and statistical models with accent on their division into discriminative and generative method. Further, the Bag-of-words method and its modification have been investigated and described. In the practical part of this work, the implementation of the Bag-of-words method with the SVM classifier was created in the Matlab environment and the model was tested on various sets of publicly available images.
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Interconnection of Restricted Boltzmann machine method with statistical physics and its implementation in the processing of spectroscopic data
Vrábel, Jakub ; Hrdlička, Aleš (referee) ; Pořízka, Pavel (advisor)
Práca sa zaoberá spojeniami medzi štatistickou fyzikou a strojovým učením s dôrazom na základné princípy a ich dôsledky. Ďalej sa venuje obecným vlastnostiam spektroskopických dát a ich zohľadnení pri pokročilom spracovaní dát. Začiatok práce je venovaný odvodeniu partičnej sumy štatistického systému a štúdiu Isingovho modelu pomocou "mean field" prístupu. Následne, popri základnom úvode do strojového učenia, je ukázaná ekvivalencia medzi Isingovým modelom a Hopfieldovou sieťou - modelom strojového učenia. Na konci teoretickej časti je z Hopfieldovej siete odvodený model Restricted Boltzmann Machine (RBM). Vhodnosť použitia RBM na spracovanie spektroskopických dát je diskutovaná a preukázaná na znížení dimenzie týchto dát. Výsledky sú porovnané s bežne používanou Metódou Hlavných Komponent (PCA), spolu so zhodnotením prístupu a možnosťami ďalšieho zlepšovania.
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Multi-Modal Restricted Boltzmann Machines
Svoboda, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This thesis explores how multi-modal Restricted Boltzmann Machines (RBM) can be used in content-based image tagging. This work also cointains brief analysis of modalities that can be used for multi-modal classification. There are also described various RBMs, that are suitable for different kinds of input data. A design and implementation of multimodal RBM is described together with results of preliminary experiments.
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Deep Neural Networks
Habrnál, Matěj ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis addresses the topic of Deep Neural Networks, in particular the methods regar- ding the field of Deep Learning, which is used to initialize the weight and learning process s itself within Deep Neural Networks. The focus is also put to the basic theory of the classical Neural Networks, which is important to comprehensive understanding of the issue. The aim of this work is to determine the optimal set of optional parameters of the algori- thms on various complexity levels of image recognition tasks through experimenting with created application applying Deep Neural Networks. Furthermore, evaluation and analysis of the results and lessons learned from the experimentation with classical and Deep Neural Networks are integrated in the thesis.
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Imputation of missing values in clinical data
BIRKLBAUER, Micha Johannes
Imputation of missing data is a crucial step in data analysis since many statistical methods require complete datasets. In that regard MissForest imputation is a powerful tool that seems to outperform most other imputation approaches. This analysis evaluates how good imputation using MissForest is compared to other methods like imputation by Multivariate Imputation by Chained Equations (MICE), Restricted Boltzmann Machines (RBM) or the standard strawman (mean) imputation in a clinical dataset that is used to predict the mortality of patients after heart valve surgery.
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Interconnection of Restricted Boltzmann machine method with statistical physics and its implementation in the processing of spectroscopic data
Vrábel, Jakub ; Hrdlička, Aleš (referee) ; Pořízka, Pavel (advisor)
Práca sa zaoberá spojeniami medzi štatistickou fyzikou a strojovým učením s dôrazom na základné princípy a ich dôsledky. Ďalej sa venuje obecným vlastnostiam spektroskopických dát a ich zohľadnení pri pokročilom spracovaní dát. Začiatok práce je venovaný odvodeniu partičnej sumy štatistického systému a štúdiu Isingovho modelu pomocou "mean field" prístupu. Následne, popri základnom úvode do strojového učenia, je ukázaná ekvivalencia medzi Isingovým modelom a Hopfieldovou sieťou - modelom strojového učenia. Na konci teoretickej časti je z Hopfieldovej siete odvodený model Restricted Boltzmann Machine (RBM). Vhodnosť použitia RBM na spracovanie spektroskopických dát je diskutovaná a preukázaná na znížení dimenzie týchto dát. Výsledky sú porovnané s bežne používanou Metódou Hlavných Komponent (PCA), spolu so zhodnotením prístupu a možnosťami ďalšieho zlepšovania.
|
|
|
Machine Learning Concepts for Categorization of Objects in Images
Hubený, Marek ; Honec, Peter (referee) ; Horák, Karel (advisor)
This work is focused on objects and scenes recognition using machine learning and computer vision tools. Before the solution of this problem has been studied basic phases of the machine learning concept and statistical models with accent on their division into discriminative and generative method. Further, the Bag-of-words method and its modification have been investigated and described. In the practical part of this work, the implementation of the Bag-of-words method with the SVM classifier was created in the Matlab environment and the model was tested on various sets of publicly available images.
|
|
|
Multi-Modal Restricted Boltzmann Machines
Svoboda, Jiří ; Beran, Vítězslav (referee) ; Hradiš, Michal (advisor)
This thesis explores how multi-modal Restricted Boltzmann Machines (RBM) can be used in content-based image tagging. This work also cointains brief analysis of modalities that can be used for multi-modal classification. There are also described various RBMs, that are suitable for different kinds of input data. A design and implementation of multimodal RBM is described together with results of preliminary experiments.
|
|
|
Deep Neural Networks
Habrnál, Matěj ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis addresses the topic of Deep Neural Networks, in particular the methods regar- ding the field of Deep Learning, which is used to initialize the weight and learning process s itself within Deep Neural Networks. The focus is also put to the basic theory of the classical Neural Networks, which is important to comprehensive understanding of the issue. The aim of this work is to determine the optimal set of optional parameters of the algori- thms on various complexity levels of image recognition tasks through experimenting with created application applying Deep Neural Networks. Furthermore, evaluation and analysis of the results and lessons learned from the experimentation with classical and Deep Neural Networks are integrated in the thesis.
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