Using extra output learning to insert a symbolic theory into a connectionist network

Minds and Machines 10 (2):171-201 (2000)
This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation.
Keywords cognitive science   connectionist theories   symbolic theories
Categories (categorize this paper)
Reprint years 2004
DOI 10.1023/A:1008313828824
 Save to my reading list
Follow the author(s)
Edit this record
My bibliography
Export citation
Find it on Scholar
Mark as duplicate
Request removal from index
Revision history
Download options
Our Archive

Upload a copy of this paper     Check publisher's policy     Papers currently archived: 31,871
Through your library
References found in this work BETA

No references found.

Add more references

Citations of this work BETA
Content and Its Vehicles in Connectionist Systems.Nicholas Shea - 2007 - Mind and Language 22 (3):246–269.

Add more citations

Similar books and articles
Added to PP index

Total downloads
9 ( #519,759 of 2,232,007 )

Recent downloads (6 months)
1 ( #445,892 of 2,232,007 )

How can I increase my downloads?

Monthly downloads
My notes
Sign in to use this feature