Multiple-Instance Learning from Pairwise Comparison Bags
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- Multiple-Instance Learning from Pairwise Comparison Bags
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Multiple-Instance Learning from Similar and Dissimilar Bags
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data MiningMultiple-instance learning (MIL) is an important weakly supervised binary classification problem, where training instances are arranged in bags, and each bag is assigned a positive or negative label. Most of the previous studies for MIL assume that ...
Multiple-instance learning with pairwise instance similarity
Abstract Multiple-Instance Learning MIL has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance ...
Multiple-Instance Learning From Unlabeled Bags With Pairwise Similarity
In <italic>multiple-instance learning</italic> (MIL), each training example is represented by a bag of instances. A training bag is either negative if it contains no positive instances or positive if it has at least one positive instance. Previous MIL ...
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![cover image ACM Transactions on Intelligent Systems and Technology](/cms/asset/bba4a809-96ba-4a35-8b64-af5ea13d21e8/3613712.cover.jpg)
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- Chongqing Science and Technology Bureau
- Fundamental Research Program
- Fundamental Research Funds for the Central Universities
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