Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Robust trainability of single neurons
Journal of Computer and System Sciences
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
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth 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
Robust Real-Time Face Detection
International Journal of Computer Vision
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Some Theory for Generalized Boosting Algorithms
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
On the value of pairwise constraints in classification and consistency
Proceedings of the 24th international conference on Machine learning
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
The Journal of Machine Learning Research
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
Proceedings of the 25th international conference on Machine learning
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Semi-Supervised Learning
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
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In supervised learning tasks, boosting can combine multiple weak learners into a stronger one. AdaBoost is one of the most popular boosting algorithms, which is widely used and stimulates extensive research efforts in the boosting research community. Different from supervised learning, semi-supervised learning aims to make full use of both labeled and unlabeled data to improve learning performance, and has drawn considerable interests in both research and applications. To harness the power of boosting, it is important and interesting to extend AdaBoost to semi-supervised scenarios. Moreover, in semi-supervised learning, it is believed that incorporating pairwise constraints such as side-information is promising to obtain more satisfiable results. However, how to extend AdaBoost with pairwise constraints remains an open problem. In this paper, we propose a novel framework to solve this problem based on the gradient descent view of boosting. The proposed framework is almost as simple and flexible as AdaBoost, and can be readily applied in the presence of pairwise constraints. We present theoretical results, show possible further extensions, and validate the effectiveness via experiments.