Abstract
We propose a large margin preference learning model based on game theory to solve the label ranking problem. Specifically, we show the proposed formulation is able to perform multiclass classification by solving a single convex optimization problem. Generally, such formulation, although theoretically well-founded, requires to learn a large number of parameters. To reduce the computational complexity, we propose a strategy based on the solution of smaller subproblems, that can be further optimized by exploiting techniques borrowed from multi-armed bandits literature. Finally, we show how the proposed framework exhibits state-of-the-art results on many benchmark datasets.
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Polato, M., Faggioli, G., Lauriola, I., Aiolli, F. (2019). Playing the Large Margin Preference Game. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_62
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