A Potential-based Framework for Online Multi-class Learning with Partial Feedback
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:900-907, 2010.
We study the problem of online multi-class learning with partial feedback: in each trial of online learning, instead of providing the true class label for a given instance, the oracle will only reveal to the learner if the predicted class label is correct. We present a general framework for online multi-class learning with partial feedback that adapts the potential-based gradient descent approaches (Cesa-Bianchi & Lugosi, 2006). The generality of the proposed framework is verified by the fact that Banditron (Kakade et al., 2008) is indeed a special case of our work if the potential function is set to be the squared $L_2$ norm of the weight vector. We propose an exponential gradient algorithm for online multi-class learning with partial feedback. Compared to the Banditron algorithm, the exponential gradient algorithm is advantageous in that its mistake bound is independent from the dimension of data, making it suitable for classifying high dimensional data. Our empirical study with four data sets show that the proposed algorithm for online learning with partial feedback is more effective than the Banditron algorithm.