Revisiting multiple instance neural networks
نویسندگان
چکیده
منابع مشابه
Revisiting multiple instance neural networks
Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In...
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Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements imprecision, and vagueness. However, there is another type of vagueness that arises when data have multiple forms of expression that fuzzy logic does not address quite well. This is the case for multiple instance learning problems (MIL). In MIL, an object is represented by a collection of instances, called a bag. A bag ...
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Multiple-Instance Learning via Embedded Instance Selection (MILES) is a recently proposed multiple-instance (MI) classification algorithm that applies a single-instance base learner to a propositionalized version of MI data. However, the original authors consider only one single-instance base learner for the algorithm — the 1-norm SVM. We present an empirical study investigating the efficacy of...
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Multi-instance learning originates from the investigation on drug activity prediction, where the task is to predict whether an unseen molecule could be used to make some drug. Such a problem is difficult because a molecule may have many alternative shapes with low energy, yet only one of those shapes may be responsible for the qualification of the molecule to make the drug. Because of its uniqu...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2018
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2017.08.026