Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
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
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to boosting and leveraging
Advanced lectures on machine learning
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Information Processing and Management: an International Journal
Efficient co-regularised least squares regression
ICML '06 Proceedings of the 23rd international conference on Machine learning
Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A discriminative model for semi-supervised learning
Journal of the ACM (JACM)
Web page and image semi-supervised classification with heterogeneous information fusion
Journal of Information Science
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We present a new generalization bound where the use of unlabeled examples results in a better ratio between training-set size and the resulting classifier's quality and thus reduce the number of labeled examples necessary for achieving it. This is achieved by demanding from the algorithms generating the classifiers to agree on the unlabeled examples. The extent of this improvement depends on the diversity of the learners—a more diverse group of learners will result in a larger improvement whereas using two copies of a single algorithm gives no advantage at all. As a proof of concept, we apply the algorithm, named AgreementBoost, to a web classification problem where an up to 40% reduction in the number of labeled examples is obtained.