Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/eswc2015/paper/poster/9> ?p ?o. }
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- 9 creator daniel-p-miranker.
- 9 creator mayank-kejriwal.
- 9 type InProceedings.
- 9 label "9".
- 9 sameAs 9.
- 9 abstract "Instance matching concerns identifying pairs of instances that refer to the same underlying entity. Current state-of-the-art instance matchers use machine learning methods. Supervised learning systems achieve good performance by training on significant amounts of manually labeled samples. To alleviate the labeling effort, this poster presents a minimally supervised instance matching approach that is able to deliver competitive performance using only 2% training data. As a first step, a committee of base classifiers is trained in an ensemble setting using boosting. Iterative semi-supervised learning is used to improve the performance of the ensemble classifier even further, by self-training it on the most confident samples labeled in the current iteration. Empirical evaluations on real-world data show that, using a multilayer perceptron as base classifier, the system is able to achieve an average F-Measure that is within 2.5% of that of state-of-the-art supervised systems.".
- 9 hasAuthorList authorList.
- 9 title "Minimally Supervised Instance Matching: An Alternate Approach".