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Keyword Suggestion in the Central Portal of Czech Libraries
Balaga, Róbert ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with various methods of keyphrase extraction from documents, specifically focused on documents from the Central Portal of Czech Libraries. Various methods from statistical, linguistic and graph-based methods have been implemented. Also a new method was suggested, that combines the statistical and linguistic approach. Individual methods have been tested and analyzed according to the standard evaluation metrics, with the suggested method achieving recall of 30 percent.
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Hybrid Methods for Nonlinear Least Squares Problems
Lukšan, Ladislav ; Matonoha, Ctirad ; Vlček, Jan
This contribution contains a description and analysis of effective methods for minimization of the nonlinear least squares function F(x) = (1=2)fT (x)f(x), where x ∈ Rn and f ∈ Rm, together with extensive computational tests and comparisons of the introduced methods. All hybrid methods are described in detail and their global convergence is proved in a unified way. Some proofs concerning trust region methods, which are difficult to find in the literature, are also added. In particular, the report contains an analysis of a new simple hybrid method with Jacobian corrections (Section 8) and an investigation of the simple hybrid method for sparse least squares problems proposed previously in [33] (Section 14).
Fulltext: content.csg -  PDF
Plný tet: 0504615-av1 - PDF
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Keyword Suggestion in the Central Portal of Czech Libraries
Balaga, Róbert ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with various methods of keyphrase extraction from documents, specifically focused on documents from the Central Portal of Czech Libraries. Various methods from statistical, linguistic and graph-based methods have been implemented. Also a new method was suggested, that combines the statistical and linguistic approach. Individual methods have been tested and analyzed according to the standard evaluation metrics, with the suggested method achieving recall of 30 percent.
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