David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:424 - 441 (1988)
The performance of a connectionist learning system on a simple problem has been described by Hinton and is briefly reviewed here: a finite set is learned from a finite collection of finite sets, and the system generalizes correctly from partial information by finding simple "features" of the environment. For comparison, a very similar problem is formulated in the Gold paradigm of discrete learning functions. To get generalization similar to the connectionist system, a non-conservative learning strategy is required. We define a simple, non-conservative strategy that generalizes like the connectionist system, finding simple "features" of the environment. By placing an arbitrary finite bound on the number and complexity of the features to be found, learning can be guaranteed relative to a probabilistic criterion of success. However, this approach to induction has essentially the same problems as many others that have failed.
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
M. R. W. Dawson, D. A. Medler, D. B. McCaughan, L. Willson & M. Carbonaro (2000). Using Extra Output Learning to Insert a Symbolic Theory Into a Connectionist Network. Minds and Machines 10 (2):171-201.
Andy Clark (1994). Representational Trajectories in Connectionist Learning. Minds and Machines 4 (3):317-32.
Vincian Gaillard, Muriel Vandenberghe, Arnaud Destrebecqz & Axel Cleeremans (2006). First and Third-Person Approaches in Implicit Learning Research. Consciousness and Cognition 15 (4):709-722.
Peter F. Dominey (1997). Reducing Problem Complexity by Analogical Transfer. Behavioral and Brain Sciences 20 (1):71-72.
Axel Cleeremans (1993). Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. MIT Press.
David J. Chalmers, What is It Like to Be a Thermostat? (Commentary on Dan Lloyd, "What is It Like to Be a Net?&Quot;).
Robert F. Hadley & M. B. Hayward (1997). Strong Semantic Systematicity From Hebbian Connectionist Learning. Minds and Machines 7 (1):1-55.
Robert F. Hadley (1994). Systematicity in Connectionist Language Learning. Mind and Language 9 (3):247-72.
Stephen Petersen (2004). Functions, Creatures, Learning, Emotion. Hudlicka and Canamero.
Axel Cleeremans & L. JimC)nez (1998). Implicit Sequence Learning: The Truth is in the Details. In Michael A. Stadler & Peter A. Frensch (eds.), Handbook of Implicit Learning. Newbury Park, CA: Sage.
David Chalmers (1992). The Evolution of Learning: An Experiment in Genetic Connectionism. In Connectionist Models: Proceedings of the 1990 Summer School Workshop. Morgan Kaufmann.
John E. Hummel (2010). Symbolic Versus Associative Learning. Cognitive Science 34 (6):958-965.
Brian P. McLaughlin & F. Warfield (1994). The Allure of Connectionism Reexamined. Synthese 101 (3):365-400.
Sorry, there are not enough data points to plot this chart.
Added to index2011-05-29
Recent downloads (6 months)0
How can I increase my downloads?