Dynamic Parameter Adaptation of SVM Based Active Learning Methodology
نویسندگان
چکیده
In this paper we present experimental assessment of a dynamic adaptation of an approach for sentiment classification of tweets. Specifically, this approach enables a dynamic adaptation of the parameters used for three-class classification with a binary SVM classifier. The approach is suited for incremental active learning scenarios in domains with frequent concept alterations and changes. Our target application is in domain of finance and the assessment is partially domain-specific, but the approach itself is not limited to a particular domain.
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