Graduate studies at Western
Minds and Machines 10 (2):171-201 (2000)
|Abstract||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)|
|Through your library||Configure|
Similar books and articles
Andy Clark (1994). Representational Trajectories in Connectionist Learning. Minds and Machines 4 (3):317-32.
Stephen Petersen (2004). Functions, Creatures, Learning, Emotion. Hudlicka and Canamero.
Brian P. McLaughlin & F. Warfield (1994). The Allure of Connectionism Reexamined. Synthese 101 (3):365-400.
Michael R. W. Dawson & C. Darren Piercey (2001). On the Subsymbolic Nature of a PDP Architecture That Uses a Nonmonotonic Activation Function. Minds and Machines 11 (2):197-218.
Istvan S. N. Berkeley (2000). What the #$*%! Is a Subsymbol? Minds and Machines 10 (1):1-13.
Dan Lloyd (1994). Connectionist Hysteria: Reducing a Freudian Case Study to a Network Model. Philosophy, Psychiatry, and Psychology 1 (2):69-88.
Robert F. Hadley & M. B. Hayward (1997). Strong Semantic Systematicity From Hebbian Connectionist Learning. Minds and Machines 7 (1):1-55.
R. C. Lacher (1993). Expert Networks: Paradigmatic Conflict, Technological Rapproachement. [REVIEW] Minds and Machines 3 (1):53-71.
Michael R. W. Dawson, D. A. Medler & Istvan S. N. Berkeley (1997). PDP Networks Can Provide Models That Are Not Mere Implementations of Classical Theories. Philosophical Psychology 10 (1):25-40.
Sorry, there are not enough data points to plot this chart.
Added to index2009-01-28
Recent downloads (6 months)0
How can I increase my downloads?