Search results
Results from the WOW.Com Content Network
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.
Alexey Ivakhnenko (Ukrainian: Олексíй Григо́рович Іва́хненко; 30 March 1913 – 16 October 2007) was a Soviet and Ukrainian mathematician most famous for developing the group method of data handling (GMDH), a method of inductive statistical learning, for which he is considered as one of the founders of deep learning.
The Group Method of Data Handling (GMDH) [5] features fully automatic structural and parametric model optimization. The node activation functions are Kolmogorov–Gabor polynomials that permit additions and multiplications. It uses a deep multilayer perceptron with eight layers. [6]
Group method of data handling, a method to train arbitrarily deep neural networks was published by Alexey Ivakhnenko and Lapa in 1967, which they regarded as a form of polynomial regression, [19] or a generalization of Rosenblatt's perceptron. [20] A 1971 paper described a deep network with eight layers trained by this method. [21]
Multiplicative operations within artificial neural networks had been studied under the names of Group Method of Data Handling (1965) [11] [12] (where Kolmogorov-Gabor polynomials implement multiplicative units or "gates" [13]), higher-order neural networks, [14] multiplication units, [15] sigma-pi units, [16] fast weight controllers, [17] and ...
However, it was not the backpropagation algorithm, and he did not have a general method for training multiple layers. In 1965, Alexey Grigorevich Ivakhnenko and Valentin Lapa published Group Method of Data Handling. It was one of the first deep learning methods, used to train an eight-layer neural net in 1971. [14] [15] [16]
A method to train multilayered perceptrons with arbitrary levels of trainable weights was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965, called the Group Method of Data Handling. This method employs incremental layer by layer training based on regression analysis, where useless units in hidden layers are pruned with the ...
A Group Method of Data Handling -type network, combined with a genetic algorithm, was used to predict the metabolizable energy of feather meal and poultry offal meal based on protein, fat, and ash content. Data samples from published literature were collected and used to train a GMDH-type network model.