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SAC
2015
ACM

Multi-view learning with dependent views

3 years 6 months ago
Multi-view learning with dependent views
Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Experiments have shown that multi-view learning is sometimes beneficial for problems for which the independence assumption is not satisfied. In practice, unfortunately, it is not possible to measure the dependency between two attribute sets; hence, there is no criterion which allows to decide whether multi-view learning is applicable. We conduct experiments with various text classification problems and investigate on the effectiveness of the co-trained SVM and the co-EM SVM under various conditions, including violations of the independence assumption. We identify the error correlation coefficient of the initial classifiers as an elaborate indicator of the expected benefit of multi-view learning. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Parameter learning General Terms Algorithms, Experimentati...
Ulf Brefeld
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SAC
Authors Ulf Brefeld
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