Reduction Scheme for Empirical Risk Minimization and Its Applications to Multiple-Instance Learning

In this paper, we propose a simple reduction scheme for empirical risk minimization (ERM) that preserves empirical Rademacher complexity. The reduction allows us to transfer known generalization bounds and algorithms for ERM to the target learning problems in a straightforward way. In particular, we apply our reduction scheme to the multiple-instance learning (MIL) problem, for which generalization bounds and ERM algorithms have been extensively studied. We show that various learning problems can be reduced to MIL. Examples include top-1 ranking learning, multi-class learning, and labeled and complementarily labeled learning. It turns out that, some of the generalization bounds derived are, despite the simplicity of derivation, incomparable or competitive with the existing bounds. Moreover, in some setting of labeled and complementarily labeled learning, the algorithm derived is the first polynomial-time algorithm

Boosting for Bounding the Worst-class Error

This paper tackles the problem of the worst-class error rate, instead of the standard error rate averaged over all classes. For example, a three-class classification task with class-wise error rates of 10\%, 10\%, and 40\% has a worst-class error rate of 40\%, whereas the average is 20\% under the class-balanced condition. The worst-class error is important in many applications. For example, in a medical image classification task, it would not be acceptable for the malignant tumor class to have a 40\% error rate, while the benign and healthy classes have 10\% error rates.We propose a boosting algorithm that guarantees an upper bound of the worst-class training error and derive its generalization bound. Experimental results show that the algorithm lowers worst-class test error rates while avoiding overfitting to the training set

MixBag: Bag-Level Data Augmentation for Learning from Label Proportions

Learning from label proportions (LLP) is a promising weakly supervised learning problem. In LLP, a set of instances (bag) has label proportions, but no instance-level labels are given. LLP aims to train an instance-level classifier by using the label proportions of the bag. In this paper, we propose a bag-level data augmentation method for LLP called MixBag, based on the key observation from our preliminary experiments; that the instance-level classification accuracy improves as the number of labeled bags increases even though the total number of instances is fixed. We also propose a confidence interval loss designed based on statistical theory to use the augmented bags effectively. To the best of our knowledge, this is the first attempt to propose bag-level data augmentation for LLP. The advantage of MixBag is that it can be applied to instance-level data augmentation techniques and any LLP method that uses the proportion loss. Experimental results demonstrate this advantage and the effectiveness of our method.Comment: Accepted at ICCV202

ランキング及び組み合わせ論的概念クラスに対する効率的な学習アルゴリズム

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Extracting Teaching Activities from E-book Logs Using Time-Series Shapelets

第20回画像の認識・理解シンポジウム（MIRU2017): ポスター予