Learning from Ambiguity

Title: en_US Learning from Ambiguity
Creator: en_US Maron, Oded
Date: 2004-10-20T20:29:24Z
Date Available: 2004-10-20T20:29:24Z
Date Issued: en_US 1998-12-01
Identifier: en_US AITR-1639
uri: http://hdl.handle.net/1721.1/7087
Abstract: en_US There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. Each example is a bag, consisting of any number of instances. A bag is labeled negative if all instances in it are negative. A bag is labeled positive if at least one instance in it is positive. Because the instances themselves are not labeled, each positive bag is an ambiguous example. We would like to learn a concept which will correctly classify unseen bags. We have developed a measure called Diverse Density and algorithms for learning from multiple-instance examples. We have applied these techniques to problems in drug design, stock prediction, and image database retrieval. These serve as examples of how to translate the ambiguity in the application domain into bags, as well as successful examples of applying Diverse Density techniques.
Extent: 11234574 bytes; 3126259 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-1639