In my project I use 2 MLP ANNs: for classification and for prediction. Basic training dataset is stored in database (MS SQL Server). But we need to make future incremental learning possible. It means that ANN is used in industrial realtime system, operator marks observations in database as objects of a particular class and ANN must take it into account. How can I do it?
If your old data is representatively of the underlying population and there is no radical shift of the underlying true model over time. Than you don't need to update you model as new data comes in. You might want to retrain you model with the new dataset periodically, but you don't need to do it in real time on your production machine.
If the underlying true does change radically overtime and you don't have any existing data to captures those changes then doesn't matter what algorithm you use, it's not going to work. The information is simply not there.