Name
Abstract | ||
|---|---|---|
The classifiers produced by the Transformation Based error-driven Learning (TBL) algorithm do not produce uncertainty measures by default. Nevertheless, there are situations like active and semi-supervised learning where the application requires both the sample's classification and the classification confidence. In this paper, we present a novel method which enables a TBL classifier to generate a probability distribution over the class labels. To assess the quality of this probability distribution, we carry out four experiments: cross entropy, perplexity, rejection curve and active learning. These experiments allow us to compare our method with another one proposed in the literature, the TBLDT. Our method, despite being simple and straightforward, outperforms TBLDT in all four experiments. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-70939-8_18 | CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing |
Keywords | Field | DocType |
rejection curve,novel method,active learning,class label,semi-supervised learning,error-driven learning,cross entropy,classification confidence,tbl classifier,probabilistic classifications,probability distribution,semi supervised learning | Cross entropy,Perplexity,Active learning,Pattern recognition,Computer science,Probability distribution,Artificial intelligence,Equivalence class,Probabilistic logic,Classifier (linguistics),Machine learning,Hash table | Conference |
Volume | ISSN | Citations |
4394 | 0302-9743 | 3 |
PageRank | References | Authors |
0.41 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cícero Nogueira dos Santos | 1 | 771 | 37.83 |
| Ruy Luiz Milidiú | 2 | 192 | 20.18 |