@InProceedings{Hirata:2005:BiImOp,
author = "Hirata, Nina Sumiko Tomita",
affiliation = "Department of Computer Science, Institute of Mathematics and
Statistics, University of Sao Paulo",
title = "Binary image operator design based on stacked generalization",
booktitle = "Proceedings...",
year = "2005",
editor = "Rodrigues, Maria Andr?ia Formico and Frery, Alejandro C?sar",
organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18.
(SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "stacked generalization, image operator design, multi-stage
training.",
abstract = "Stacked generalization refers to any learning schema that consists
of multiple levels of training. Level zero classifiers are those
that depend solely on input data while classifiers at other levels
may use the output of lower levels as the input. Stacked
generalization can be used to address the difficulties related to
the design of image operators defined on large windows. This paper
describes a simple stacked generalization schema for the design of
binary image operators and presents several application examples
that show its effectiveness as a training schema.",
conference-location = "Natal, RN, Brazil",
conference-year = "9-12 Oct. 2005",
doi = "10.1109/SIBGRAPI.2005.16",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2005.16",
language = "en",
ibi = "6qtX3pFwXQZeBBx/GLv3s",
url = "http://urlib.net/ibi/6qtX3pFwXQZeBBx/GLv3s",
targetfile = "hiratan.pdf",
urlaccessdate = "2024, Apr. 29"
}