Fuzzy c-means based support vector machines classifier for perfume recognition

2016 ◽  
Vol 46 ◽  
pp. 452-458 ◽  
Author(s):  
Engin Esme ◽  
Bekir Karlik
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Fuzzy C Means ◽  
Vector Machines

Study on PMV Index Forecasting Method Based on Fuzzy C-Means Clustering

2011 ◽  
Vol 383-390 ◽  
pp. 925-930
Author(s):  
Chun Cheng Zhang ◽  
Xiang Guang Chen ◽  
Yuan Qing Xu
Keyword(s):  
Support Vector Machines ◽  
Clustering Algorithm ◽  
Large Data ◽  
Support Vector ◽  
Data Sets ◽  
Forecasting Accuracy ◽  
Fuzzy C Means ◽  
Forecasting Method ◽  
Vector Machines ◽  
Fuzzy C Means Clustering

In order to improve the forecasting accuracy of indoor thermal comfort, the basic principle of fuzzy c-means clustering algorithm (FCM) and support vector machines (SVM) is analyzed. A kind of SVM forecasting method based on FCM data preprocess is proposed in this paper. The large data sets can be divided into multiple mixed groups and each group is represented by a single regression model using the proposed method. The support vector machines based on fuzzy c-means clustering algorithm (FCM+SVM) and the BP neural network based on fuzzy c-means clustering algorithm (FCM+BPNN) are respectively applied to forecast PMV index. The experimental results demonstrate that the FCM+SVM method has better forecasting accuracy compared with FCM+BPNN method.

scholarly journals Fuzzy c-Means Clustering Using Kernel Functions in Support Vector Machines

2003 ◽  
Vol 7 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Sadaaki Miyamoto ◽  
◽  
Daisuke Suizu ◽  
Keyword(s):  
Support Vector Machines ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Kernel Functions ◽  
Inner Product ◽  
Support Vector ◽  
Fuzzy C Means ◽  
Vector Machines ◽  
Minimization Algorithms ◽  
Fuzzy C Means Clustering

We studied clustering algorithms of fuzzy c-means using a kernel to represent an inner product for mapping into high-dimensional space. Such kernels have been studied in support vector machines used by many researchers in pattern classification. Algorithms of fuzzy c-means are transformed into kernel-based methods by changing objective functions, whereby new iterative minimization algorithms are derived. Numerical examples show that clusters that cannot be obtained without a kernel are generated.

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