Publication Details
Title: Simplified ART: A New Class of ART Algorithms
Author: A. Baraldi and E. Alpaydin
Group: ICSI Technical Reports
Date: February 1998
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1998/tr-98-004.pdf
Overview:
The Simplified Adaptive Resonance Theory (SART) class of networks is pro posed to handle problems encountered in Adaptive Resonance Theory 1 (ART 1)-based algorithms when detection of binary and analog patterns is performed. The basic idea of SART is to substitute ART 1-based "unidirectional" (asymmetric) activation and match functions with "bidirectional" (symmetric) function pairs. This substitution makes the class of SART algorithms potentially more robust and less time-consuming than ART 1-based systems. One SART algorithm, termed Fuzzy SART, is discussed. Fuzzy SART employs probabilistic and possibilistic fuzzy membership functions to combine soft competitive learning with outlier detection. Its soft competitive strategy relates Fuzzy SART to the well-known Self-Organizing Map and Neural Gas clustering algorithm. A new Normalized Vector Distance, which can be employed by Fuzzy SART, is also presented. Fuzzy SART performs better than ART 1-based Carpenter-Grossberg-Rosen Fuzzy ART in the clustering of a simple two-dimensional data set and the standard four-dimensional IRIS data set. As expected, Fuzzy SART is less sensitive than Fuzzy ART to small changes in input parameters and in the order of the presentation sequence. In the clustering of the IRIS data set, performances of Fuzzy SART are analogous to or better than those of several clustering models found in the literature. Keywords: hard and soft competitive learning, cluster detection, ART 1-based systems, Self-Organizing Map, Neural Gas algorithm, fuzzy set theory, fuzzy clustering
Bibliographic Information:
ICSI Technical Report TR-98-004
Bibliographic Reference:
A. Baraldi and E. Alpaydin. Simplified ART: A New Class of ART Algorithms. ICSI Technical Report TR-98-004, February 1998
Author: A. Baraldi and E. Alpaydin
Group: ICSI Technical Reports
Date: February 1998
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1998/tr-98-004.pdf
Overview:
The Simplified Adaptive Resonance Theory (SART) class of networks is pro posed to handle problems encountered in Adaptive Resonance Theory 1 (ART 1)-based algorithms when detection of binary and analog patterns is performed. The basic idea of SART is to substitute ART 1-based "unidirectional" (asymmetric) activation and match functions with "bidirectional" (symmetric) function pairs. This substitution makes the class of SART algorithms potentially more robust and less time-consuming than ART 1-based systems. One SART algorithm, termed Fuzzy SART, is discussed. Fuzzy SART employs probabilistic and possibilistic fuzzy membership functions to combine soft competitive learning with outlier detection. Its soft competitive strategy relates Fuzzy SART to the well-known Self-Organizing Map and Neural Gas clustering algorithm. A new Normalized Vector Distance, which can be employed by Fuzzy SART, is also presented. Fuzzy SART performs better than ART 1-based Carpenter-Grossberg-Rosen Fuzzy ART in the clustering of a simple two-dimensional data set and the standard four-dimensional IRIS data set. As expected, Fuzzy SART is less sensitive than Fuzzy ART to small changes in input parameters and in the order of the presentation sequence. In the clustering of the IRIS data set, performances of Fuzzy SART are analogous to or better than those of several clustering models found in the literature. Keywords: hard and soft competitive learning, cluster detection, ART 1-based systems, Self-Organizing Map, Neural Gas algorithm, fuzzy set theory, fuzzy clustering
Bibliographic Information:
ICSI Technical Report TR-98-004
Bibliographic Reference:
A. Baraldi and E. Alpaydin. Simplified ART: A New Class of ART Algorithms. ICSI Technical Report TR-98-004, February 1998