Original Contribution: Stacked generalization
Neural Networks
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Ensembling neural networks: many could be better than all
Artificial Intelligence
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Boosting a Strong Learner: Evidence Against the Minimum Margin
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Improve Decision Trees for Probability-Based Ranking by Lazy Learners
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Learning with unlabeled data and its application to image retrieval
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Combining committee-based semi-supervised learning and active learning
Journal of Computer Science and Technology
Semi-supervised classification with active query selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In many learning tasks, there are abundant unlabeled samples but the number of labeled training samples is limited, because labeling the samples requires the efforts of human annotators and expertise. There are three major techniques for labeling the samples: semi-supervised learning, transductive learning and active learning. Semi-supervised and transductive learning deal with methods for automated exploiting unlabeled samples in addition to improve learning performance. Active learning deals with methods that assume the learner has control over the whole input space. So combing the advantage of semi-supervised learning and active learning is a practical technique for improving the learning performance. In this paper, a general framework of combing (Active Learning) AL and (Semi-Supervised Learning) SSL algorithms is proposed. Then the ensemble learning for combing AL and SSL algorithms is introduced, which is denoted by ASC (AL and SSL by Committee). At last, the ensemble learning and confidence measure of the ASC is discussed.