The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
"Modular Neural Networks ?"
marcelvanvelzen
Member Posts: 1 
Learner III
Hi,
Is it possible to specify more modular Neural Networks in RapidMiner ? For example, the 4-bit parity problem is best solved by splitting the problem into two examplesets and each having their own learner. So, in total there are three neural networks working together.
If possible, how can I specify this ?
Regards,
Marcel
Is it possible to specify more modular Neural Networks in RapidMiner ? For example, the 4-bit parity problem is best solved by splitting the problem into two examplesets and each having their own learner. So, in total there are three neural networks working together.
If possible, how can I specify this ?
Regards,
Marcel
Tagged:
0
Answers
sure. You could build two example sets by "copying" the data set with the IOMultiplier (don't worry, the data is actually not copied but only a view on it) and apply different ExampleFilter operator so that the data is splitted into the two desired example sets. Then you can apply two learners on both data sets on their own. You can now create predictions of the models and join them with the ExampleSetJoin operator. Then change the predictions to regular attributes with the ChangeAttributeRole operator and learn the final model on it.
It's a bit tricky but it should be possible. Of course, you could also write your own operator performing all these steps at once but I actually would always prefer this more modular way of working since you can more insight and control over the subprocesses.
Cheers,
Ingo