Abstract
Machine learning indicates methods and algorithms which allow a model to learn a behavior thanks to examples. Active learning gathers methods which select examples used to build a training set for the predictive model. All the strategies aim to use the less examples as possible and to select the most informative examples. After having formalized the active learning problem and after having located it in the literature, this article synthesizes in the first part the main approaches of active learning. Taking into account emotions in Human-machine interactions can be helpful for intelligent systems designing. The main difficulty, for the conception of calls center’s automatic shunting system, is the cost of data labeling. The last section of this paper propose to reduce this cost thanks to two active learning strategies. The study is based on real data resulting from the use of a vocal stock exchange server.
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Bondu, A., Lemaire, V., Poulain, B. (2007). Active Learning Strategies: A Case Study for Detection of Emotions in Speech. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_18
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DOI: https://doi.org/10.1007/978-3-540-73435-2_18
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