The Informational Complexity of Learning from Examples

Title: en_US The Informational Complexity of Learning from Examples
Creator: en_US Niyogi, Partha
Date: 2004-10-20T20:28:05Z
Date Available: 2004-10-20T20:28:05Z
Date Issued: en_US 1996-09-01
Identifier: en_US AITR-1587
uri: http://hdl.handle.net/1721.1/7069
Abstract: en_US This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in the principles and parameters framework of modern linguistic theory. These problems are analyzed from the perspective of computational learning theory and certain unifying perspectives emerge.
Extent: 3260099 bytes; 3332017 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-1587