Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Chun-Ru Dong"'
Publikováno v:
International Journal of Foundations of Computer Science. :1-20
Unsupervised image clustering is a challenging task in computer vision. Recently, various deep clustering algorithms based on contrastive learning have achieved promising performance and some distinguishable features representation were obtained only
Publikováno v:
Information Fusion. 88:296-304
Publikováno v:
Computers and Electrical Engineering. 104:108457
Publikováno v:
Knowledge-Based Systems. 253:109507
Publikováno v:
IEEE Transactions on Fuzzy Systems. 23:1638-1654
We investigate essential relationships between generalization capabilities and fuzziness of fuzzy classifiers (viz., the classifiers whose outputs are vectors of membership grades of a pattern to the individual classes). The study makes a claim and o
Publikováno v:
Neurocomputing. 146:95-103
In this work, we propose an optimization model to tune feature weights for improving performance of clustering via a minimization of uncertainty (fuzziness and non-specificity) of its similarity matrix among objects. To solve the proposed model effic
Autor:
Chun-Ru Dong, Xizhao Wang
Publikováno v:
IEEE Transactions on Fuzzy Systems. 17:556-567
When fuzzy IF-THEN rules initially extracted from data have not a satisfying performance, we consider that the rules require refinement. Distinct from most existing rule-refinement approaches that are based on the further reduction of training error,
Publikováno v:
ICMLC
Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, we design an optimization model t
Publikováno v:
ICMLC
The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vecto
Publikováno v:
ICMLC
One of the most important issues in fuzzy decision tree learning is the fuzzification of input data. This paper proposes a self-adaptive data fuzzification algorithm based on the self-organizing map (SOM) technology, which can automatically determine