A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Graph transduction via alternating minimization
Proceedings of the 25th international conference on Machine learning
Weakly-supervised acquisition of labeled class instances using graph random walks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Experiments in graph-based semi-supervised learning methods for class-instance acquisition
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatic selection of radiological protocols using machine learning
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
Harvesting facts from textual web sources by constrained label propagation
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 1st international workshop on Search and mining entity-relationship data
Coupled temporal scoping of relational facts
Proceedings of the fifth ACM international conference on Web search and data mining
Twitter polarity classification with label propagation over lexical links and the follower graph
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Querying provenance for ranking and recommending
TaPP'12 Proceedings of the 4th USENIX conference on Theory and Practice of Provenance
Graph-based lexicon expansion with sparsity-inducing penalties
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Coupling label propagation and constraints for temporal fact extraction
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Bilingual lexicon extraction from comparable corpora using label propagation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Collectively representing semi-structured data from the web
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
Acquiring temporal constraints between relations
Proceedings of the 21st ACM international conference on Information and knowledge management
PRAVDA-live: interactive knowledge harvesting
Proceedings of the 21st ACM international conference on Information and knowledge management
Graph-Based transduction with confidence
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
PIDGIN: ontology alignment using web text as interlingua
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
WebChild: harvesting and organizing commonsense knowledge from the web
Proceedings of the 7th ACM international conference on Web search and data mining
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We propose a new graph-based label propagation algorithm for transductive learning. Each example is associated with a vertex in an undirected graph and a weighted edge between two vertices represents similarity between the two corresponding example. We build on Adsorption, a recently proposed algorithm and analyze its properties. We then state our learning algorithm as a convex optimization problem over multi-label assignments and derive an efficient algorithm to solve this problem. We state the conditions under which our algorithm is guaranteed to converge. We provide experimental evidence on various real-world datasets demonstrating the effectiveness of our algorithm over other algorithms for such problems. We also show that our algorithm can be extended to incorporate additional prior information, and demonstrate it with classifying data where the labels are not mutually exclusive.