Validating Co-Training Models for Web Image Classification
Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/7438 |
| _version_ | 1826215513010733056 |
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| author | Zhang, Dell Lee, Wee Sun |
| author_facet | Zhang, Dell Lee, Wee Sun |
| author_sort | Zhang, Dell |
| collection | MIT |
| description | Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present. |
| first_indexed | 2024-09-23T16:32:37Z |
| format | Article |
| id | mit-1721.1/7438 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:32:37Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/74382019-04-11T02:45:32Z Validating Co-Training Models for Web Image Classification Zhang, Dell Lee, Wee Sun Co-Training Machine Learning Multimedia Data Mining Semi-Supervised Learning Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present. Singapore-MIT Alliance (SMA) 2004-12-13T08:16:21Z 2004-12-13T08:16:21Z 2005-01 Article http://hdl.handle.net/1721.1/7438 en Computer Science (CS); 148397 bytes application/pdf application/pdf |
| spellingShingle | Co-Training Machine Learning Multimedia Data Mining Semi-Supervised Learning Zhang, Dell Lee, Wee Sun Validating Co-Training Models for Web Image Classification |
| title | Validating Co-Training Models for Web Image Classification |
| title_full | Validating Co-Training Models for Web Image Classification |
| title_fullStr | Validating Co-Training Models for Web Image Classification |
| title_full_unstemmed | Validating Co-Training Models for Web Image Classification |
| title_short | Validating Co-Training Models for Web Image Classification |
| title_sort | validating co training models for web image classification |
| topic | Co-Training Machine Learning Multimedia Data Mining Semi-Supervised Learning |
| url | http://hdl.handle.net/1721.1/7438 |
| work_keys_str_mv | AT zhangdell validatingcotrainingmodelsforwebimageclassification AT leeweesun validatingcotrainingmodelsforwebimageclassification |