Name
Abstract | ||
|---|---|---|
Recently, Semi-Supervised learning algorithms such as co-training are used in many domains. In co-training, two classifiers based on different subsets of the features or on different learning algorithms are trained in parallel and unlabeled data that are classified differently by the classifiers but for which one classifier has large confidence are labeled and used as training data for the other. In this paper, a new form of co-training, called Ensemble-Co-Training, is proposed that uses an ensemble of different learning algorithms. Based on a theorem by Angluin and Laird that relates noise in the data to the error of hypotheses learned from these data, we propose a criterion for finding a subset of high-confidence predictions and error rate for a classifier in each iteration of the training process. Experiments show that the new method in almost all domains gives better results than the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICTAI.2011.126 | ICTAI |
Keywords | Field | DocType |
training data,better result,error rate,semi-supervised learning,new method,disagreement-based co-training,different learning algorithm,new form,different subsets,unlabeled data,training process,prediction algorithms,semi supervised learning,learning artificial intelligence,boosting,ensemble learning,decision trees,decision tree,labeling | Training set,Decision tree,Semi-supervised learning,Pattern recognition,Computer science,Word error rate,Co-training,Boosting (machine learning),Artificial intelligence,Classifier (linguistics),Ensemble learning,Machine learning | Conference |
Citations | PageRank | References |
10 | 0.54 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jafar Tanha | 1 | 50 | 4.17 |
| Maarten van Someren | 2 | 402 | 48.51 |
| Hamideh Afsarmanesh | 3 | 1890 | 291.69 |