TY - GEN
T1 - Leveraging active learning for relevance feedback using an information theoretic diversity measure
AU - Dagli, Charlie K.
AU - Rajaram, Shyamsundar
AU - Huang, Thomas S.
PY - 2006
Y1 - 2006
N2 - Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard techniques that have received much attention towards solving this interactive learning problem. How to best utilize the user's effort for labeling, however, remains unanswered. It has been shown in the past that labeling a diverse set of points is helpful, however, the notion of diversity has either been dependent on the learner used, or computationally expensive. In this paper, we intend to address these issues by proposing a fundamentally motivated, information-theoretic view of diversity and its use in a fast, non-degenerate active learning-based relevance feedback setting. Comparative testing and results are reported and thoughts for future work are presented.
AB - Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard techniques that have received much attention towards solving this interactive learning problem. How to best utilize the user's effort for labeling, however, remains unanswered. It has been shown in the past that labeling a diverse set of points is helpful, however, the notion of diversity has either been dependent on the learner used, or computationally expensive. In this paper, we intend to address these issues by proposing a fundamentally motivated, information-theoretic view of diversity and its use in a fast, non-degenerate active learning-based relevance feedback setting. Comparative testing and results are reported and thoughts for future work are presented.
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U2 - 10.1007/11788034_13
DO - 10.1007/11788034_13
M3 - Conference contribution
AN - SCOPUS:33746634025
SN - 3540360182
SN - 9783540360186
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 123
EP - 132
BT - Image and Video Retrieval - 5th International Conference, CIVR 2006, Proceedings
PB - Springer
T2 - 5th International Conference on Image and Video Retrieval, CIVR 2006
Y2 - 13 July 2006 through 15 July 2006
ER -