A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning Bayesian Networks
A local algorithm for finding dense subgraphs
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Extracting Moving People from Internet Videos
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Towards google challenge: combining contextual and social information for web video categorization
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Image classification using the web graph
Proceedings of the international conference on Multimedia
Semantic label sharing for learning with many categories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Handling label noise in video classification via multiple instance learning
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Enriching media fragments with named entities for video classification
Proceedings of the 22nd international conference on World Wide Web companion
Exploiting socially-generated side information in dimensionality reduction
Proceedings of the 2nd international workshop on Socially-aware multimedia
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Classification of web-based videos is an important task with many applications in video search and ads targeting. However, collecting labeled data needed for classifier training may be prohibitively expensive. Semi-supervised learning provides a possible solution whereby inexpensive but noisy weakly-labeled data is used instead. In this paper, we explore an approach which exploits YouTube video co-watch data to improve the performance of a video taxonomic classification system. A graph is built whereby edges are created based on video co-watch relationships and weakly-labeled videos are selected for classifier training through local graph clustering. Evaluation is performed by comparing against classifiers trained using manually labeled web documents and videos. We find that data collected through the proposed approach can be used to train competitive classifiers versus the state of the art, particularly in the absence of expensive manually-labeled data.