Spectral fuzzy classification system: a supervised approach. Spectral fuzzy classification system: a supervised approach.

Del Amo, Ana, Gómez, Daniel, and Montero, Javier. "Spectral fuzzy classification system: a supervised approach.." Mathware and Soft Computing 10.2-3 (2003): 141-154. <http://eudml.org/doc/39256>.

@article{DelAmo2003,
abstract = {The goal of this paper is to present all algorithm for pattern recognition, leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing. The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy Sets Theory (see [14]), approaching remotely sensed classification problems as multicriteria decision making problems, solved by means of an outranking methodology (see [12] and also [11]). The referred algorithm was a unsupervised classification algorithm, but now in this paper will present a modification of the original algorithm into a supervised version.},
author = {Del Amo, Ana, Gómez, Daniel, Montero, Javier},
journal = {Mathware and Soft Computing},
keywords = {Lógica difusa; Reconocimiento de formas; Algoritmos de clasificación; Teledetección},
language = {eng},
number = {2-3},
pages = {141-154},
title = {Spectral fuzzy classification system: a supervised approach.},
url = {http://eudml.org/doc/39256},
volume = {10},
year = {2003},
}

TY - JOUR
AU - Del Amo, Ana
AU - Gómez, Daniel
AU - Montero, Javier
TI - Spectral fuzzy classification system: a supervised approach.
JO - Mathware and Soft Computing
PY - 2003
VL - 10
IS - 2-3
SP - 141
EP - 154
AB - The goal of this paper is to present all algorithm for pattern recognition, leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing. The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy Sets Theory (see [14]), approaching remotely sensed classification problems as multicriteria decision making problems, solved by means of an outranking methodology (see [12] and also [11]). The referred algorithm was a unsupervised classification algorithm, but now in this paper will present a modification of the original algorithm into a supervised version.
LA - eng
KW - Lógica difusa; Reconocimiento de formas; Algoritmos de clasificación; Teledetección
UR - http://eudml.org/doc/39256
ER -