Computational Intelligence in Pattern Recognition

2022 ◽  
Keyword(s):  
Pattern Recognition ◽  
Computational Intelligence

scholarly journals THEORETICAL ANALYSIS OF PICTURE FUZZY CLUSTERING: CONVERGENCE AND PROPERTY

2018 ◽  
Vol 34 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Pham Thi Minh Phuong ◽  
Pham Huy Thong ◽  
Le Hoang Son
Keyword(s):  
Pattern Recognition ◽  
Theoretical Analysis ◽  
Fuzzy Clustering ◽  
Computational Intelligence ◽  
Local Minimum ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Mathematical Properties ◽  
Fuzzy Clustering Algorithm ◽  
Similarities And Differences

Recently, picture fuzzy clustering (FC-PFS) has been introduced as a new computational intelligence tool for various problems in knowledge discovery and pattern recognition. However, an important question that was lacked in the related researches is examination of mathematical properties behind the picture fuzzy clustering algorithm such as the convergence, the boundary or the convergence rate, etc. In this paper, we will prove that FC-PFS converges to at least one local minimum. The similarities and differences between this algorithm and other clustering methods are compared. Analysis on the loss function is also considered.

Artificial Intelligence Algorithms for Classification and Pattern Recognition

2018 ◽  
pp. 350-376
Author(s):  
Robert Jarušek ◽  
Vaclav Kocian
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Soft Computing ◽  
Computational Intelligence ◽  
Computational Algorithms ◽  
Training Algorithms ◽  
Correct Interpretation ◽  
Rule Based ◽  
Computer Based ◽  
Classification Tasks

Classification tasks can be solved using so-called classifiers. A classifier is a computer based agent which can perform a classification task. There are many computational algorithms that can be utilized for classification purposes. Classifiers can be broadly divided into two categories: rule-based classifiers and computational intelligence based classifiers usually called soft computing. Rule-based classifiers are generally constructed by the designer, where the designer defines rules for the interpretation of detected inputs. This is in contrast to soft-computing based classifiers, where the designer only creates a basic framework for the interpretation of data. The learning or training algorithms within such systems are responsible for the generation of rules for the correct interpretation of data.

Artificial Intelligence Algorithms for Classification and Pattern Recognition

2017 ◽  
pp. 53-85
Author(s):  
Robert Jarušek ◽  
Vaclav Kocian
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Soft Computing ◽  
Computational Intelligence ◽  
Computational Algorithms ◽  
Training Algorithms ◽  
Correct Interpretation ◽  
Rule Based ◽  
Computer Based ◽  
Classification Tasks

Classification tasks can be solved using so-called classifiers. A classifier is a computer based agent which can perform a classification task. There are many computational algorithms that can be utilized for classification purposes. Classifiers can be broadly divided into two categories: rule-based classifiers and computational intelligence based classifiers usually called soft computing. Rule-based classifiers are generally constructed by the designer, where the designer defines rules for the interpretation of detected inputs. This is in contrast to soft-computing based classifiers, where the designer only creates a basic framework for the interpretation of data. The learning or training algorithms within such systems are responsible for the generation of rules for the correct interpretation of data.

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