Abstract
In this paper, we proposed an incremental max-margin model for semi-supervised multi-classification learning, where efficient and accuracy need to be considered. Three notable properties are introduced: (1) the model predicts a label for unlabeled sample instance in runtime, and trained with the complete sample instance, while unlabeled and labeled sample instances are unified in our objective function; (2) since the objective function of our model is convex, we can design efficient online algorithm with logarithmic regret, it achieve accurate solution with very little overhead; (3) our model is max-margin machine, which provide our model with considerable generalization capability for future unseen data. Our approach captures essences of the exploration-exploitation tradeoff.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No.61379069) and the Key Technologies R&D Program of China (No.2014BAK09B04).
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Hu, T., Yu, J. (2016). Incremental Max-Margin Learning for Semi-Supervised Multi-Class Problem. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015. Studies in Computational Intelligence, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-23509-7_3
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DOI: https://doi.org/10.1007/978-3-319-23509-7_3
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