A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Learning polynomial networks for classification of clinical electroencephalograms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
Letters: Energy demand prediction using GMDH networks
Neurocomputing
Software Reliability Prediction Using Group Method of Data Handling
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Data mining via rules extracted from GMDH: an application to predict churn in bank credit cards
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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In this paper, a novel Kernel based Soft Computing hybrid viz., Kernel Group Method of Data Handling (KGMDH) is proposed to solve regression problems. In the proposed KGMDH technique, Kernel trick is employed on the input data in order to get Kernel matrix, which in turn becomes input to GMDH. Several experiments are conducted on five benchmark regression datasets to assess the effectiveness of the proposed technique. The results and a statistical t-test conducted thereof indicate that the proposed KGMDH yields more accurate results than the standalone GMDH in most datasets. This is the significant outcome of the study.