J. Chen, and C. Chen. Neural Networks, IEEE Transactions on, 13 (6):
1364--1373(2002)
Abstract
A new learning method, the fuzzy kernel perceptron (FKP), in which
the fuzzy perceptron (FP) and the Mercer kernels are incorporated,
is proposed in this paper. The proposed method first maps the input
data into a high-dimensional feature space using some implicit mapping
functions. Then, the FP is adopted to find a linear separating hyperplane
in the high-dimensional feature space. Compared with the FP, the
FKP is more suitable for solving the linearly nonseparable problems.
In addition, it is also more efficient than the kernel perceptron
(KP). Experimental results show that the FKP has better classification
performance than FP, KP, and the support vector machine.
%0 Journal Article
%1 Chen2002
%A Chen, Jiun-Hung
%A Chen, Chu-Song
%D 2002
%J Neural Networks, IEEE Transactions on
%K (artificial Mercer classification, feature functions, fuzzy high-dimensional intelligence), kernel, kernel-based learning learning, machine mapping method, nets, neural pattern perceptron, perceptrons, space, supervised support vector
%N 6
%P 1364--1373
%T Fuzzy kernel perceptron
%V 13
%X A new learning method, the fuzzy kernel perceptron (FKP), in which
the fuzzy perceptron (FP) and the Mercer kernels are incorporated,
is proposed in this paper. The proposed method first maps the input
data into a high-dimensional feature space using some implicit mapping
functions. Then, the FP is adopted to find a linear separating hyperplane
in the high-dimensional feature space. Compared with the FP, the
FKP is more suitable for solving the linearly nonseparable problems.
In addition, it is also more efficient than the kernel perceptron
(KP). Experimental results show that the FKP has better classification
performance than FP, KP, and the support vector machine.
@article{Chen2002,
abstract = {A new learning method, the fuzzy kernel perceptron (FKP), in which
the fuzzy perceptron (FP) and the Mercer kernels are incorporated,
is proposed in this paper. The proposed method first maps the input
data into a high-dimensional feature space using some implicit mapping
functions. Then, the FP is adopted to find a linear separating hyperplane
in the high-dimensional feature space. Compared with the FP, the
FKP is more suitable for solving the linearly nonseparable problems.
In addition, it is also more efficient than the kernel perceptron
(KP). Experimental results show that the FKP has better classification
performance than FP, KP, and the support vector machine.},
added-at = {2009-09-12T19:19:34.000+0200},
author = {Chen, Jiun-Hung and Chen, Chu-Song},
biburl = {https://www.bibsonomy.org/bibtex/27c6177195f6fbccf9c645961f94adc1f/mozaher},
file = {01058073.pdf:concept disambiguation\\context\\20070203\\01058073.pdf:PDF},
interhash = {957f4d7b5b9a0592733dcffd0d5b64ff},
intrahash = {7c6177195f6fbccf9c645961f94adc1f},
issn = {1045-9227},
journal = {Neural Networks, IEEE Transactions on},
keywords = {(artificial Mercer classification, feature functions, fuzzy high-dimensional intelligence), kernel, kernel-based learning learning, machine mapping method, nets, neural pattern perceptron, perceptrons, space, supervised support vector},
number = 6,
owner = {Mozaher},
pages = {1364--1373},
timestamp = {2009-09-12T19:19:38.000+0200},
title = {Fuzzy kernel perceptron},
volume = 13,
year = 2002
}