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Knowledge Extraction with BP-netwoks
Reitermanová, Zuzana
Title: Knowledge Extraction with BP-networks Author: Zuzana Reitermanová Department: Katedra softwarového inženýrství Supervisor: Doc. RNDr. Iveta Mrázová, CSc. Supervisor's e-mail address: mrazova@ksi.ms.mff.cuni.cz Abstract: Multi-layered neural networks of the back-propagation type are well known for their universal approximation capability. Already the stan- dard back-propagation training algorithm used for their adjustment provides often applicable results. However, efficient solutions to complex tasks cur- rently dealt with require a quick convergence and a transparent network structure. This supports both an improved generalization capability of the formed networks and an easier interpretation of their function later on. Var- ious techniques used to optimize the structure of the networks like learning with hints; pruning and sensitivity analysis are expected to impact a bet- ter generalization, too. One of the fast learning algorithms is the conjugate gradient method. In this thesis, we discuss, test and analyze the above-mentioned methods. Then, we derive a new technique combining together the advantages of them. The proposed algorithm is based on the rapid scaled conjugate gradient tech- nique. This classical method is enhanced with the enforcement of a transpar- ent internal knowledge...
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Social networks and data mining
Zvirinský, Peter ; Mrázová, Iveta (advisor) ; Neruda, Roman (referee)
Recent data mining methods represent modern approaches capable of analyzing large amounts of data and extracting meaningful and potentially useful information from it. In this work, we discuss all the essential steps of the data mining process - including data preparation, storage, cleaning, data analysis as well as visualization of the obtained results. In particular, this work is focused on the data available publicly from the Insolvency Register of the Czech Republic, that comprises all insolvency proceedings commenced after 1. January 2008 in the Czech Republic. With regard to the considered type of data, several data mining methods have been discussed, implemented, tested and evaluated. Among others, the studied techniques include Market Basket Analysis, Bayesian networks and social network analysis. The obtained results reveal several social patterns common in the current Czech society.
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Knowledge Extraction with BP-netwoks
Reitermanová, Zuzana
Title: Knowledge Extraction with BP-networks Author: Zuzana Reitermanová Department: Katedra softwarového inženýrství Supervisor: Doc. RNDr. Iveta Mrázová, CSc. Supervisor's e-mail address: mrazova@ksi.ms.mff.cuni.cz Abstract: Multi-layered neural networks of the back-propagation type are well known for their universal approximation capability. Already the stan- dard back-propagation training algorithm used for their adjustment provides often applicable results. However, efficient solutions to complex tasks cur- rently dealt with require a quick convergence and a transparent network structure. This supports both an improved generalization capability of the formed networks and an easier interpretation of their function later on. Var- ious techniques used to optimize the structure of the networks like learning with hints; pruning and sensitivity analysis are expected to impact a bet- ter generalization, too. One of the fast learning algorithms is the conjugate gradient method. In this thesis, we discuss, test and analyze the above-mentioned methods. Then, we derive a new technique combining together the advantages of them. The proposed algorithm is based on the rapid scaled conjugate gradient tech- nique. This classical method is enhanced with the enforcement of a transpar- ent internal knowledge...
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Extraction of unspecified relations from the web
Ovečka, Marek ; Svátek, Vojtěch (advisor) ; Labský, Martin (referee)
The subject of this thesis is non-specific knowledge extraction from the web. In recent years, tools that improve the results of this type of knowledge extraction were created. The aim of this thesis is to become familiar with these tools, test and propose the use of results. In this thesis these tools are described and compared and extraction is carried out using OLLIE. Based on the results of the extractions, two methods of enriching extractions using name entity recognition, are proposed. The first method proposes to modify the weights of extractions and second proposes the enrichment of extractions by named entities. The paper proposed ontology, which allows to capture the structure of enriched extractions. In the last part practical experiment is carried out, in which the proposed methods are demonstrated. Future research in this field would be useful in areas of extraction and categorization of relational phrases.
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