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Classification of eMail Communication
Piják, Marek ; Herout, Adam (referee) ; Szőke, Igor (advisor)
This diploma's thesis is based around creating a classifier, which will be able to recognize an email communication received by Topefekt.s.r.o on daily basis and assigning it into classification class. This project will implement some of the most commonly used classification methods including machine learning. Thesis will also include evaluation comparing all used methods.
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Machine Learning in Image Classification
Král, Jiří ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
This project deals vith analysis and testing of algorithms and statistical models, that could potentionaly improve resuts of FIT BUT in ImageNet Large Scale Visual Recognition Challenge and TRECVID. Multinomial model was tested. Phonotactic Intersession Variation Compensation (PIVCO) model was used for reducing random e ffects in image representation and for dimensionality reduction. PIVCO - dimensionality reduction achieved the best mean average precision while reducing to one-twenyth of original dimension. KPCA model was tested to approximate Kernel SVM. All statistical models were tested on Pascal VOC 2007 dataset.
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Dimensionality reduction of statistical dataset
Sabo, Adam ; Kosová, Petra (referee) ; Hrabec, Pavel (advisor)
This thesis introduces methods which are used to reduce dimensionality and their subsequent application to selected sets of sports statistical data. The first part of the thesis deals with the theoretical apparatus of mathematical statistics, in particular with the Principal Component Analysis and its alternative - the Factor Analysis. The second part provides a brief explanation of the terms related to the selected sets of football statistics where these methods are applied. The third part introduces the results of the application of both methods to statistical files. Data obtained through calculations performed in Python programming language are organized and interpreted by means of graphs and tables.
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High-performance exploration and querying of selected multi-dimensional spaces in life sciences
Kratochvíl, Miroslav ; Bednárek, David (advisor) ; Glaab, Enrico (referee) ; Svozil, Daniel (referee)
This thesis studies, implements and experiments with specific application-oriented approaches for exploring and querying multi-dimensional datasets. The first part of the thesis scrutinizes indexing of the complex space of chemical compounds, and details a design of high-performance retrieval system for small molecules. The resulting system is then utilized within a wider context of federated search in heterogeneous data and metadata related to the chemical datasets. In the second part, the thesis focuses on fast visualization and exploration of many-dimensional data that originate from single- cell cytometry. Self-organizing maps are used to derive fast methods for analysis of the datasets, and used as a base for a novel data visualization algorithm. Finally, a similar approach is utilized for highly interactive exploration of multimedia datasets. The main contributions of the thesis comprise the advancement in optimization and methods for querying the chemical data implemented in the Sachem database cartridge, the federated, SPARQL-based interface to Sachem that provides the heterogeneous search support, dimensionality reduction algorithm EmbedSOM, design and implementation of the specific EmbedSOM-backed analysis tool for flow and mass cytometry, and design and implementation of the multimedia...
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Dimensionality reduction of statistical dataset
Sabo, Adam ; Kosová, Petra (referee) ; Hrabec, Pavel (advisor)
This thesis introduces methods which are used to reduce dimensionality and their subsequent application to selected sets of sports statistical data. The first part of the thesis deals with the theoretical apparatus of mathematical statistics, in particular with the Principal Component Analysis and its alternative - the Factor Analysis. The second part provides a brief explanation of the terms related to the selected sets of football statistics where these methods are applied. The third part introduces the results of the application of both methods to statistical files. Data obtained through calculations performed in Python programming language are organized and interpreted by means of graphs and tables.
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High-performance exploration and querying of selected multi-dimensional spaces in life sciences
Kratochvíl, Miroslav ; Bednárek, David (advisor) ; Glaab, Enrico (referee) ; Svozil, Daniel (referee)
This thesis studies, implements and experiments with specific application-oriented approaches for exploring and querying multi-dimensional datasets. The first part of the thesis scrutinizes indexing of the complex space of chemical compounds, and details a design of high-performance retrieval system for small molecules. The resulting system is then utilized within a wider context of federated search in heterogeneous data and metadata related to the chemical datasets. In the second part, the thesis focuses on fast visualization and exploration of many-dimensional data that originate from single- cell cytometry. Self-organizing maps are used to derive fast methods for analysis of the datasets, and used as a base for a novel data visualization algorithm. Finally, a similar approach is utilized for highly interactive exploration of multimedia datasets. The main contributions of the thesis comprise the advancement in optimization and methods for querying the chemical data implemented in the Sachem database cartridge, the federated, SPARQL-based interface to Sachem that provides the heterogeneous search support, dimensionality reduction algorithm EmbedSOM, design and implementation of the specific EmbedSOM-backed analysis tool for flow and mass cytometry, and design and implementation of the multimedia...
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Classification of eMail Communication
Piják, Marek ; Herout, Adam (referee) ; Szőke, Igor (advisor)
This diploma's thesis is based around creating a classifier, which will be able to recognize an email communication received by Topefekt.s.r.o on daily basis and assigning it into classification class. This project will implement some of the most commonly used classification methods including machine learning. Thesis will also include evaluation comparing all used methods.
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Machine Learning in Image Classification
Král, Jiří ; Španěl, Michal (referee) ; Hradiš, Michal (advisor)
This project deals vith analysis and testing of algorithms and statistical models, that could potentionaly improve resuts of FIT BUT in ImageNet Large Scale Visual Recognition Challenge and TRECVID. Multinomial model was tested. Phonotactic Intersession Variation Compensation (PIVCO) model was used for reducing random e ffects in image representation and for dimensionality reduction. PIVCO - dimensionality reduction achieved the best mean average precision while reducing to one-twenyth of original dimension. KPCA model was tested to approximate Kernel SVM. All statistical models were tested on Pascal VOC 2007 dataset.
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Utilization of advanced statistical methods for processing of florescence emission of plants affected by local biotic stress
MATOUŠ, Karel
Chlorophyll fluorescence imaging is noninvasive technique often used in plant physiology, molecular biology and precision farming. Captured sequences of images record the dynamic of chlorophyll fluorescence emission which contain the information about spatial and time changes of photosynthetic activity of plant. The goal of this Ph.D. thesis is to contribute to the development of chlorophyll fluorescence imaging by application of advanced statistical techniques. Methods of statistical pattern recognition allow to identify images in the captured sequence that are reach for information about observed biotic stress and to find small subsets of fluorescence images suitable for following analysis. I utilized only methods for identification of small sets of images providing high performance with realistic time consumptions.
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