Talk:Group method of data handling
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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.
- You are right. Almost all GMDH algorithms sort-out gradually changing models and check them by external criterion. Even OCC algorithm. Perelom 12:44, 30 October 2007 (UTC)
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.
- Of course. But the main here that it is done simultaneously in GMDH. From the three classes of criteria (accuracy, balance and information type) usually is used criteria of accuracy. Perelom 12:44, 30 October 2007 (UTC)
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.
- Difference of the GMDH algorithms from another algorithms of structural identification and best regression selection algorithms consists of several main peculiarities, which I think must be added to the page:
- -usage of external criteria, which are based on data sample dividing and are adequate to problem of forecasting models construction;
- -more diversity of structure generators: usage like in regression algorithms of the ways of full or reduced sorting of structure variants and of original multilayered (iteration) procedures;
- -better level of automatization: there are needed to enter initial data sample and type of external criterion only;
- -automatic adaptation of optimal model complexity and external criteria to level of noises or statistical violations - effect of noiseimmunity cause robustness of the approach;
- -implementation of principle of inconclusive decisions in process of gradual models complication. Perelom 13:25, 30 October 2007 (UTC)
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.
- No, the Combinatorial is pure 'inductive' sorting GMDH algorithm. By the way, it make full sorting of models with not only increasing, but also decreasing complexity. Perelom 12:44, 30 October 2007 (UTC)