COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Image Processing
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
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Supervised learning algorithms (relevance feedback (RF) algorithms) are often used in content based image retrieval (CBIR) systems to enhance interactive search and browsing of image databases. One of the issues associated with RF based CBIR systems is the lack of a large training set. Labeling of images is a time consuming activity and user's usually do not have the patience to label a large set. The challenge is to somehow leverage the much larger set of unlabeled images to improve the performance of CBIR systems. In this paper we propose a novel RF algorithm which learns from both labeled and unlabeled data. Our proposed algorithm also uses active learning so as to maximize the information gained from a given amount of user feedback.