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dc.title | Lenses classification by means of pseudo neural networks - Two approaches | en |
dc.contributor.author | Komínková Oplatková, Zuzana | |
dc.contributor.author | Šenkeřík, Roman | |
dc.relation.ispartof | MENDEL 2014 | |
dc.identifier.issn | 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR | |
dc.identifier.isbn | 9788021449848 | |
dc.date.issued | 2014 | |
utb.relation.volume | 2014-January | |
utb.relation.issue | January | |
dc.citation.spage | 397 | |
dc.citation.epage | 402 | |
dc.event.title | 20th International Conference on Soft Computing: Evolutionary Computation, Genetic Programming, Swarm Intelligence, Fuzzy Logic, Neural Networks, Fractals, Bayesian Methods, MENDEL 2014 | |
dc.event.location | Brno | |
utb.event.state-en | Czech Republic | |
utb.event.state-cs | Česká republika | |
dc.event.sdate | 2014-06-25 | |
dc.event.edate | 2014-06-27 | |
dc.type | conferenceObject | |
dc.language.iso | en | |
dc.publisher | Brno University of Technology | |
dc.subject | Analytic programming | en |
dc.subject | Artificial neural networks | en |
dc.subject | Classifiers | en |
dc.subject | Evolutionary computation | en |
dc.subject | Optimization | en |
dc.subject | Pseudo neural networks | en |
dc.subject | Symbolic regression | en |
dc.description.abstract | This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure, which serves as a classifier. This structure is similar to classical artificial neural net therefore the name pseudo neural network is used. The proposed method for classifier structure synthesis utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The paper shows two approaches - continues classification with one output node and classical approach with binary classification and more output nodes. Lenses data (one of benchmarks for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used. | en |
utb.faculty | Faculty of Applied Informatics | |
dc.identifier.uri | http://hdl.handle.net/10563/1005268 | |
utb.identifier.obdid | 43871863 | |
utb.identifier.scopus | 2-s2.0-84938078069 | |
utb.source | d-scopus | |
dc.date.accessioned | 2015-08-28T12:04:58Z | |
dc.date.available | 2015-08-28T12:04:58Z | |
utb.contributor.internalauthor | Komínková Oplatková, Zuzana | |
utb.contributor.internalauthor | Šenkeřík, Roman |