National Repository of Grey Literature 58 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Ljubopytnov, Vladimír ; Húsek, Dušan (referee) ; Pokorný, Jaroslav (advisor)
This thesis focuses on mapping latest knowledge in the area of web mining with emphasis on document clustering. Most attention is given to the DOC projective clustering algorithm, a modification is presented for data with weighted dimensions. Algorithm is used for web search engine result clustering. Also, a clustering package with Google interface and phrase evaluation tool is implemented.
Clusters of closely related documents
Diviš, Jiří ; Húsek, Dušan (referee) ; Holub, Martin (advisor)
This thesis focuses on automatic searching for clusters of topically similar texts in large text collection. We introduce an algorithm for nding the clusters and a method of optimizing its parameters using machine learning techniques. The algorithm is implemented and experimentaly evaluated. For evaluation we use a manually annotated collection of Czech documents, which contains a set of sample clusters chosen and tagged by a human annotator, and a huge collection of newspaper arcticles. Experiments show that the output of our algorithm ful ls our expectation and gives clusters of topically similar texts.
Biologicky inspirované modely založené na prototypech a aplikace gompertzovské dynamiky ve shlukové analýze
Pastorek, Lukáš ; Řezanková, Hana (advisor) ; Húsek, Dušan (referee) ; Nánásiová, Oľga (referee)
The thesis deals with the analysis of the clustering and mapping techniques derived from the principles of the neural and statistical learning and growth theory. The selected branch of the unsupervised bio-inspired prototype-based models is described in terms of the proposed logical framework, which highlights the continuity of these methods with the classical "pure" statistical methods. Moreover, as those methods are broadly understood as the "black boxes" with the unpredictable, unclear and especially hidden behavior, the examples of the spatial computational and organizational patterns in two-dimensional space are provided. Additionally, this thesis presents the novel concept based on the non-linear, non-Gaussian Gompertzian function, which has been widely used as the universal law in dynamic growth models, but has not yet been applied in the field of computational intelligence. The essence of Gompertzian dynamics is mathematically analyzed and a novel simple version of the Gompertzian normalized function is introduced. Furthermore, the function was modified for use in the field of artificial intelligence and neural implications were discussed. Additionally, the novel neural networks were proposed and derived from the topological principles of Kohonen's self-organizing maps and neural gas algorithm. The Gompertzian networks were evaluated using several indicators for various generated and real datasets. Gompertzian neural networks with fixed grid and integrated neighborhood ranking principle generally show lower mean squared errors than the original SOM algorithms. Likewise, the unconstrained Gompertzian networks have demonstrated overall low error rates comparable to neural gas algorithm, more stable and lower error solutions than the k- means sequential procedure. In conclusion, the Gompertzian function has been shown to be a viable concept and an effective computational tool for multidimensional data analysis.
Source localization for EEG patterns relevant to motor imagery BCI control
Bobrov, P. ; Frolov, A. ; Húsek, Dušan ; Tintěra, J.
This work concerns spatial localization of sources of EEG patterns the most specific for control of the motor imagery based BCI. In our previous work we have shown that performance of Bayesian BCI classifier can be drastically improved by extraction of the most relevant independent components of the EEG signal. This paper presents the results of spatial localization of electrical brain activity sources which activity is reflected by the extracted components. The localization was performed by solving the inverse problem in EEG source localization, using individual finite-element head models. The sources were located in central sulcus (Brodmann area 3a), in the superior regions of post- and precentral gyri, and supplementary motor cortex.
Artificial Intelligence Methods in geoinformatics
Voženílek, V. ; Dvorský, J. ; Húsek, Dušan
The book "Artificial Intelligence Methods in geoinformatics", (editors: Voženílek, V. - Dvorsky, J. - Husek, D) issued by the University of Palacky in Olomouc (in 2011. 184 pp. ISBN 978-80-244-2945-8) is the final outcome of the project GACR 205/09/1079 - "Artificial intelligence methods in GIS." The aim of the book was to summarize the results of research on the application of artificial intelligence methods for processing, visualization and interpretation of spatial information in the area of geographic information systems. The project was a joint research activity of experts from various research fields of Palacky University in Olomouc, VSB-Technical University of Ostrava, and Institute of Computer Science, Academy of Sciences Czech Republic targeted to the integration of methods used in solving problems of artificial intelligence and methods of processing spatial data, whose integration is essential for the development of new modern research methods and technologies.
Cluster analysis of large data sets: new procedures based on the method k-means
Žambochová, Marta ; Řezanková, Hana (advisor) ; Húsek, Dušan (referee) ; Antoch, Jaromír (referee)
Abstract Cluster analysis has become one of the main tools used in extracting knowledge from data, which is known as data mining. In this area of data analysis, data of large dimensions are often processed, both in the number of objects and in the number of variables, which characterize the objects. Many methods for data clustering have been developed. One of the most widely used is a k-means method, which is suitable for clustering data sets containing large number of objects. It is based on finding the best clustering in relation to the initial distribution of objects into clusters and subsequent step-by-step redistribution of objects belonging to the clusters by the optimization function. The aim of this Ph.D. thesis was a comparison of selected variants of existing k-means methods, detailed characterization of their positive and negative characte- ristics, new alternatives of this method and experimental comparisons with existing approaches. These objectives were met. I focused on modifications of the k-means method for clustering of large number of objects in my work, specifically on the algorithms BIRCH k-means, filtering, k-means++ and two-phases. I watched the time complexity of algorithms, the effect of initialization distribution and outliers, the validity of the resulting clusters. Two real data files and some generated data sets were used. The common and different features of method, which are under investigation, are summarized at the end of the work. The main aim and benefit of the work is to devise my modifications, solving the bottlenecks of the basic procedure and of the existing variants, their programming and verification. Some modifications brought accelerate the processing. The application of the main ideas of algorithm k-means++ brought to other variants of k-means method better results of clustering. The most significant of the proposed changes is a modification of the filtering algorithm, which brings an entirely new feature of the algorithm, which is the detection of outliers. The accompanying CD is enclosed. It includes the source code of programs written in MATLAB development environment. Programs were created specifically for the purpose of this work and are intended for experimental use. The CD also contains the data files used for various experiments.

National Repository of Grey Literature : 58 records found   1 - 10nextend  jump to record:
See also: similar author names
1 HUŠEK, David
2 Husek, Daniel
Interested in being notified about new results for this query?
Subscribe to the RSS feed.