Talk:Group method of data handling

It is obviously hard to define what is GMDH. Since it is a set of algorithms the definition should be a set of its common properties I think.

In the description of GMDH: "..it simultaneously minimize the models error and find out the optimal model structure.." the phrase "minimize the models error" is not a property of GMDH. This is a property of a criterion of regularity but, there are a lot of other criteria for which this is not truth.

As far as I understand, the only principle of GMDH that is really common for all algorithms is the 'search of a model of optimal complexity' this principle makes us to use 'sample dividing' and gives us 'noise resistance'. It is used in combinatorial, multilayered and harmonic algorithms for sure.

The second, inductiveness is a property of only multilayered GMDH i.e. property of GMDH-type NNs. I can't see any inductiveness in the combinatorial algorithm because models are not 'gradually complicated'. Perhaps that is not good but that is the way it works.