Inny tytuł:

Książka = Book ; KS/3/2012/T1P[9]

Wydawca:

Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences

Miejsce wydania:

Warszawa

Opis:

[6], 129-141 pages ; 21 cm ; Bibliography p. 140-141

Typ obiektu:

Book/Chapter

Abstrakt:

Importance of feature selection techniques in multidimensional data analysis is nowadays beyond doubt. It is especially so in such learning tasks which are characterized by a very high dimensionality and a low number of learning examples. An alternative approach to well known and commonly used selection methods (e.g. backward, forward, stepwise) is to use the Akaike Information Criterion (AIC) for feature selection investigating the whole feature set simultaneously. An experimental approach to feature selection suggested in the paper is based on so-called AIC Improvement Matrices, which describe the situation in the whole feature set. Besides paying attention to AIC selection algorithms refer also to correlation between features in the data set.

Czasopismo/Seria/cykl:

Książka = Book

Szczegółowy typ zasobu:

Book

Identyfikator zasobu:

oai:rcin.org.pl:198680

Źródło:

KS-2012-03-T1P[9]

Język:

eng

Język streszczenia:

eng

Prawa:

Creative Commons Attribution BY 4.0 license

Zasady wykorzystania:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitalizacja:

Systems Research Institute of the Polish Academy of Sciences

Lokalizacja oryginału:

Library of Systems Research Institute PAS

Dofinansowane ze środków:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.

Dostęp:

Open