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Character recognition in the soundtrack with SOM
Malásek, Jan ; Honzík, Petr (referee) ; Honzík, Petr (referee) ; Pohl, Jan (advisor)
This bachelor´s thesis describes a history of neural networks evolution and their using in speech recognition systems and shows problems with working and learning neural networks. It presents three chosen systems for speech recognition including their evaluation in experiments, their advantages and disadvantages. It is also about human speech characteristics and systems of its recognition. The last part is focused on frequency spectrums of different types of vowels and gives instructions for programming neural networks using MATLAB.
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Photogrammetric data storage
Malásek, Jan ; Petyovský, Petr (referee) ; Babinec, Tomáš (advisor)
The Bachelor´s thesis gives us a view of computer vision development and its applications in a real world. It exists many typical tasks of computer vision. This thesis is focused on the scene description of picture information. It describes the most widely used types of relational databases, their advantages and disadvantages. The most important part of the work is a realization of application for photogrammetric data service. It is programmed in C# using Microsoft Visual Studio 2010. The application is based on a relational database system Microsoft SQL Server Compact Edition 3.5.
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Effect of HFS Based Feature Selection on Cluster Analysis
Malásek, Jan ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
Master´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.
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Character recognition in the soundtrack with SOM
Malásek, Jan ; Honzík, Petr (referee) ; Honzík, Petr (referee) ; Pohl, Jan (advisor)
This bachelor´s thesis describes a history of neural networks evolution and their using in speech recognition systems and shows problems with working and learning neural networks. It presents three chosen systems for speech recognition including their evaluation in experiments, their advantages and disadvantages. It is also about human speech characteristics and systems of its recognition. The last part is focused on frequency spectrums of different types of vowels and gives instructions for programming neural networks using MATLAB.
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Photogrammetric data storage
Malásek, Jan ; Petyovský, Petr (referee) ; Babinec, Tomáš (advisor)
The Bachelor´s thesis gives us a view of computer vision development and its applications in a real world. It exists many typical tasks of computer vision. This thesis is focused on the scene description of picture information. It describes the most widely used types of relational databases, their advantages and disadvantages. The most important part of the work is a realization of application for photogrammetric data service. It is programmed in C# using Microsoft Visual Studio 2010. The application is based on a relational database system Microsoft SQL Server Compact Edition 3.5.
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|
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Effect of HFS Based Feature Selection on Cluster Analysis
Malásek, Jan ; Klusáček, Jan (referee) ; Honzík, Petr (advisor)
Master´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.
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