I am not sure if this is the right place to ask this but here goes:
Sometimes times two or more inputs of a neural networks can often be related to a single "real world" entity.
weight of a person to predict the probability of disease in population or
volume of a stock to predict the market.
When a single training set contains data about a number of these entities, how can we make a neural network understand that two inputs (or more) are related to the same entity?
Amongst all the people I have asked, the general consensus seems to be:
- Neural Networks do not work this way
- It is not possible
- Such a grouping of data is not required
- Neural Networks are supposed to find the relationship amongst inputs, you are not supposed to feed it the relationships
- The training data set need to be reworked / reconfigured
- I have never heard of such a thing
So, obviously this is not in the mainstream. Has anyone heard of any research in this direction?
P.S. If you agree with the above opinion (it can't / shouldn't be done) please provide a reason why.