Group method of data handling

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 <a href="/facts/Data_mining/AH53q5ac">data mining</a>, <a href="/facts/Knowledge_discovery/eopEv228">knowledge discovery</a>, <a href="/facts/Forecasting/pwFtGyzM">prediction</a>, <a href="/facts/Complex_systems/eGG3B1nn">complex systems</a> modeling, <a href="/facts/Optimization_(mathematics)/oRn8Iv5I">optimization</a> and <a href="/facts/Pattern_recognition/o1s38opj">pattern recognition</a>. 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 external criterion. The last section of contains a summary of the applications of GMDH in the 1970s.
Other names include "polynomial feedforward neural network", or "self-organization of models". It was one of the first <a href="/facts/Deep_learning/JLuwD3ea">deep learning</a> methods, used to train an eight-layer neural net in 1971.

Group method of data handling open-in-new

Group method of data handling