Learning from Ambiguity
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 la...
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| Language: | en_US |
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2004
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| Online Access: | http://hdl.handle.net/1721.1/7087 |
| _version_ | 1826217651948486656 |
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| author | Maron, Oded |
| author_facet | Maron, Oded |
| author_sort | Maron, Oded |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T17:07:06Z |
| id | mit-1721.1/7087 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:07:06Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/70872019-04-10T11:52:34Z Learning from Ambiguity Maron, Oded 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. 2004-10-20T20:29:24Z 2004-10-20T20:29:24Z 1998-12-01 AITR-1639 http://hdl.handle.net/1721.1/7087 en_US AITR-1639 11234574 bytes 3126259 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | Maron, Oded Learning from Ambiguity |
| title | Learning from Ambiguity |
| title_full | Learning from Ambiguity |
| title_fullStr | Learning from Ambiguity |
| title_full_unstemmed | Learning from Ambiguity |
| title_short | Learning from Ambiguity |
| title_sort | learning from ambiguity |
| url | http://hdl.handle.net/1721.1/7087 |
| work_keys_str_mv | AT maronoded learningfromambiguity |