Training radial basis function classifiers with Gaussian kernel clustering and fuzzy decision technique

1995 ◽  
Author(s):  
Yuntao Qian ◽  
Weixing Xie
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Gaussian Kernel ◽  
Fuzzy Decision ◽  
Kernel Clustering ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

Technical data mining with evolutionary radial basis function classifiers

2009 ◽  
Vol 9 (2) ◽  
pp. 765-774 ◽  
Author(s):  
Markus Bauer ◽  
Oliver Buchtala ◽  
Timo Horeis ◽  
Ralf Kern ◽  
Bernhard Sick ◽  
...  
Keyword(s):  
Data Mining ◽  
Radial Basis Function ◽  
Basis Function ◽  
Technical Data ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

scholarly journals Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
S. N. Deepa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Minimum Mean Square Error ◽  
Gaussian Kernel ◽  
Neural Network Classifier ◽  
Real Coded Genetic Algorithm ◽  
Radial Basis

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

scholarly journals Comparing support vector machines with Gaussian kernels to radial basis function classifiers

1997 ◽  
Vol 45 (11) ◽  
pp. 2758-2765 ◽  
Author(s):  
B. Scholkopf ◽  
Kah-Kay Sung ◽  
C.J.C. Burges ◽  
F. Girosi ◽  
P. Niyogi ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Radial Basis Function ◽  
Basis Function ◽  
Support Vector ◽  
Gaussian Kernels ◽  
Vector Machines ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

scholarly journals Unified Noise Reduction using Adaptive Radial Basis Function

2019 ◽  
Vol 8 (9) ◽  
pp. 484-490
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Noise Reduction ◽  
Kernel Function ◽  
Basis Function ◽  
Gaussian Noise ◽  
Speckle Noise ◽  
Gaussian Kernel ◽  
Noisy Image ◽  
Radial Basis

The images captured by SAR and sonar are blurred and corrupted more by speckle noise and also other types of noise like Gaussian noise and salt & pepper noise. Denoising all types of noises to get perfect image is a vital challenge, earlier works on the same mode addressed with one filter for one noise, there is no one common or unified filter which can denoise all types of noise. Therefore in this paper, we have designed a filter which not only removes speckle noise, but also combination of other noises. Here IUNR (Intelligent Unified Noise Reduction) algorithm is proposed which is based on neural network called adaptive radial basis function acts as a unified filter for Denoising. Proposed method needs a single noisy image to train the adaptive radial basis function neural network to learn the correction of the noisy image. The Gaussian kernel function is applied to reconstruct the local disturbance appeared because of the noise. The proposed adaptive radial basis function network is compared with the fixed form which has fixed spread and the center value of kernel function. This method can correct the image suffered from different varieties of noises like speckle noise, salt & pepper noise and Gaussian noise separately or combination of noise. Various standard test images are considered for test purpose with different levels of noise density and performance of proposed algorithm is compared with adaptive wiener filter.

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