Learning with partly labeled data
Authors: Bouchachia, A.
Journal: Neural Computing and Applications
Volume: 16
Issue: 3
Pages: 267-293
ISSN: 0941-0643
DOI: 10.1007/s00521-007-0091-0
Abstract:Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of a classifier. This paper outlines the two main classes of learning methods to deal with partly labeled data: pre-labeling-based learning and semi-supervised learning. Concretely, we introduce and discuss three methods from each class. The first three ones are two-stage methods consisting of selecting the data to be labeled and then training the classifier using the pre-labeled and the originally labeled data. The last three ones show how labeled and unlabeled data can be combined in a symbiotic way during training. The empirical evaluation of these methods shows: (1) pre-labeling methods tend be better than semi-supervised learning methods, (2) both labeled and unlabeled have positive effect on the classification accuracy of each of the proposed methods, (3) the combination of all the methods improve the accuracy, and (4) the proposed methods compare very well with the state-of-art methods. © 2007 Springer-Verlag London Limited.
Source: Scopus
Learning with partly labeled data
Authors: Bouchachia, A.
Journal: NEURAL COMPUTING & APPLICATIONS
Volume: 16
Issue: 3
Pages: 267-293
eISSN: 1433-3058
ISSN: 0941-0643
DOI: 10.1007/s00521-007-0091-0
Source: Web of Science (Lite)
Learning with partly labeled data.
Authors: Bouchachia, A.
Journal: Neural Comput. Appl.
Volume: 16
Pages: 267-293
DOI: 10.1007/s00521-007-0091-0
Source: DBLP
Preferred by: Hamid Bouchachia