scholarly journals DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING

2012 ◽  
Vol 2 (3) ◽  
pp. 348-352 ◽  
Author(s):  
Safish Mary M ◽  
◽  
Joseph Raj V ◽  
Keyword(s):  
Radial Basis Function ◽  
Hierarchical Clustering ◽  
Data Reduction ◽  
Basis Function ◽  
Data Classification ◽  
Radial Basis ◽  
Neural Classifier

scholarly journals Data Classification With Radial Basis Function Networks Based on a Novel Kernel Density Estimation Algorithm

2005 ◽  
Vol 16 (1) ◽  
pp. 225-236 ◽  
Author(s):  
Y.-J. Oyang ◽  
S.-C. Hwang ◽  
Y.-Y. Ou ◽  
C.-Y. Chen ◽  
Z.-W. Chen
Keyword(s):  
Radial Basis Function ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Basis Function ◽  
Kernel Density ◽  
Data Classification ◽  
Estimation Algorithm ◽  
Radial Basis Function Networks ◽  
Radial Basis

Adaptive radial basis function mesh deformation using data reduction

2016 ◽  
Vol 321 ◽  
pp. 997-1025 ◽  
Author(s):  
T. Gillebaart ◽  
D.S. Blom ◽  
A.H. van Zuijlen ◽  
H. Bijl
Keyword(s):  
Radial Basis Function ◽  
Data Reduction ◽  
Basis Function ◽  
Mesh Deformation ◽  
Radial Basis ◽  
Using Data

Replacement based non-linear data reduction in radial basis function networks QSAR modeling

2014 ◽  
Vol 135 ◽  
pp. 157-165 ◽  
Author(s):  
Atefe Malek-Khatabi ◽  
Mohsen Kompany-Zareh ◽  
Somayeh Gholami ◽  
Saeed Bagheri
Keyword(s):  
Radial Basis Function ◽  
Data Reduction ◽  
Basis Function ◽  
Radial Basis Function Networks ◽  
Qsar Modeling ◽  
Non Linear ◽  
Radial Basis

scholarly journals Educational Data Classification using Data Mining and Kernel Ensemble Classifier

2019 ◽  
Vol 9 (2S4) ◽  
pp. 667-670
Keyword(s):  
Data Mining ◽  
Radial Basis Function ◽  
Basis Function ◽  
Data Classification ◽  
Ensemble Classifier ◽  
Ensemble Classification ◽  
Support Vector ◽  
Linear Polynomial ◽  
Radial Basis ◽  
Using Data

The success of students gives the good name for institution and it become popular. Due to the large number of student’s database it is difficult to identify the performance and activities of each student. The educational data mining is used to identify the performance and status of the students individually. In this study, the Educational Data Classification (EDC) using data mining technique and kernel ensemble classification using Support Vector Machine (SVM) based kernels like linear, polynomial, quadratic and Radial Basis Function (RBF) is discussed. Initially the data preprocessing is made to remove the raw data into understandable format. The SVM kernels like linear, polynomial, quadratic and radial basis function based ensemble classifier is used for classification of student’s data. The data mining is used for making final decision of student’s performance in class like activities and interaction with electronic learning system. The performance of the system is evaluated by kalboard 360 database. The performance of the system is made by classification accuracy of 72.52% using SVM kernel ensemble classification

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