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Wyszukujesz frazę "conditional inference trees" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
A Multivariate Study of T/V Forms in European Languages Based on a Parallel Corpus of Film Subtitles
Autorzy:
Levshina, Natalia
Tematy:
T/V pronouns
politeness
film subtitles
conditional inference trees
Pokaż więcej
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Powiązania:
https://bibliotekanauki.pl/articles/620953.pdf  Link otwiera się w nowym oknie
Opis:
The present study investigates the cross-linguistic differences in the use of so-called T/V forms (e.g. French tu and vous, German du and Sie, Russian ty and vy) in ten European languages from different language families and genera. These constraints represent an elusive object of investigation because they depend on a large number of subtle contextual features and social distinctions, which should be cross-linguistically matched. Film subtitles in different languages offer a convenient solution because the situations of communication between film characters can serve as comparative concepts. I selected more than two hundred contexts that contain the pronouns you and yourself in the original English versions, which are then coded for fifteen contextual variables that describe the Speaker and the Hearer, their relationships and different situational properties. The creators of subtitles in the other languages have to choose between T and V when translating from English, where the T/V distinction is not expressed grammatically. On the basis of these situations translated in ten languages, I perform multivariate analyses using the method of conditional inference trees in order to identify the most relevant contextual variables that constrain the T/V variation in each language.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of tree-based methods used in survival data
Autorzy:
Yabaci, Aysegul
Sigirli, Deniz
Tematy:
tree-based methods
conditional inference trees
conditional inference forests
random survival forests
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Wydawca:
Główny Urząd Statystyczny
Powiązania:
https://bibliotekanauki.pl/articles/2034119.pdf  Link otwiera się w nowym oknie
Opis:
Survival trees and forests are popular non-parametric alternatives to parametric and semiparametric survival models. Conditional inference trees (Ctree) form a non-parametric class of regression trees embedding tree-structured regression models into a well-defined theory of conditional inference procedures. The Ctree is applicable in a varietyof regression-related issues, involving nominal, ordinal, numeric, censored, as well as multivariate response variables and arbitrary measurement scales of covariates. Conditional inference forests (Cforest) consitute a survival forest method which combines a large number of Ctrees. The Cforest provides a unified and flexible framework for ensemble learning in the presence of censoring. The random survival forests (RSF) methodology extends the random forests method enabling the approximation of rich classes of functions while maintaining generalisation errors low. In the present study, the Ctree, Cforest and RSF methods are discussed in detail and the performances of the survival forest methods, namely the Cforest and RSF have been compared with a simulation study. The results of the simulation demonstrate that the RSF method with a log-rank score distinction criteria outperforms the Cforest and the RSF with log-rank distinction criteria.
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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