Not enough data to create a plot.
Try a different view from the menu above.
Fass, David
Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories
Fass, David, Feldman, Jacob
We present an account of human concept learning-that is, learning of categories from examples-based on the principle of minimum description length (MDL). In support of this theory, we tested a wide range of two-dimensional concept types, including both regular (simple) and highly irregular (complex) structures, and found the MDL theory to give a good account of subjects' performance. This suggests that the intrinsic complexity ofa concept (that is, its description -length) systematically influences its leamability.
Categorization Under Complexity: A Unified MDL Account of Human Learning of Regular and Irregular Categories
Fass, David, Feldman, Jacob
We present an account of human concept learning-that is, learning of categories from examples-based on the principle of minimum description length(MDL). In support of this theory, we tested a wide range of two-dimensional concept types, including both regular (simple) and highly irregular (complex) structures, and found the MDL theory to give a good account of subjects' performance. This suggests that the intrinsic complexityofa concept (that is, its description -length) systematically influences its leamability.