Journal Article10.1007/S11390-006-0800-7
Multi-Instance Learning from Supervised View
TL;DR: This paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from thediscrimination on the instances to the discrimination on the bags, and proposes to build multi- instance ensembles to solve multi- instances problems.
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Abstract: In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners.
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Citations
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TL;DR: This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, Where the training data are given with only coarse-grained labels; and inaccurate supervision,Where the given labels are not always ground-truth.
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Weakly supervised machine learning
TL;DR: Weakly supervised learning as discussed by the authors aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground-truth value.
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Breast Ultrasound Image Classification Based on Multiple-Instance Learning
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