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- Group_method_of_data_handling abstract "Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models.GMDH is used in such fields as data mining, knowledge discovery, prediction, complex systems modeling, optimization and pattern recognition.GMDH algorithms are characterized by inductive procedure that performs sorting-out of gradually complicated polynomial models and selecting the best solution by means of the so-called external criterion.A GMDH model with multiple inputs and one output is a subset of components of the base function (1): where f are elementary functions dependent on different sets of inputs, a are coefficients and m is the number of the base function components.In order to find the best solution GMDH algorithms consider various component subsets of the base function (1) called partial models. Coefficients of these models are estimated by the least squares method. GMDH algorithms gradually increase the number of partial model components and find a model structure with optimal complexity indicated by the minimum value of an external criterion. This process is called self-organization of models.The most popular base function used in GMDH is the gradually complicated Kolmogorov-Gabor polynomial (2): GMDH is also known as polynomial neural networks and statistical learning networks thanks to implementation of the corresponding algorithms in several commercial software products.".
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- Group_method_of_data_handling subject Category:Classification_algorithms.
- Group_method_of_data_handling subject Category:Computational_statistics.
- Group_method_of_data_handling subject Category:Neural_networks.
- Group_method_of_data_handling subject Category:Regression_variable_selection.
- Group_method_of_data_handling type Abstraction100002137.
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- Group_method_of_data_handling comment "Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models.GMDH is used in such fields as data mining, knowledge discovery, prediction, complex systems modeling, optimization and pattern recognition.GMDH algorithms are characterized by inductive procedure that performs sorting-out of gradually complicated polynomial models and selecting the best solution by means of the so-called external criterion.A GMDH model with multiple inputs and one output is a subset of components of the base function (1): where f are elementary functions dependent on different sets of inputs, a are coefficients and m is the number of the base function components.In order to find the best solution GMDH algorithms consider various component subsets of the base function (1) called partial models. ".
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