منابع مشابه
Multi-instance multi-label learning
In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the Miml...
متن کاملMulti-instance multi-label active learning
Multi-instance multi-label learning (MIML) has achieved success in various applications, especially those involving complicated learning objects. Along with the enhancing of expressive power, the cost of annotating a MIML example also increases significantly. In this paper, we propose a novel active learning approach to reduce the labeling cost of MIML. The approach actively query the most valu...
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Multi-Instance Multi-Label learning (MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, there lacks theoretical exploration to the learnability of MIML. In this paper, through proving a ...
متن کاملFast Multi-Instance Multi-Label Learning
In multi-instance multi-label learning (MIML), one object is represented by multiple instances and simultaneously associated with multiple labels. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderatesized data. To efficiently handle large data sets, we propose the MIMLfast approach, which first constructs a low-dimensional subspace...
متن کاملConstrained instance clustering in multi-instance multi-label learning
In multi-instance multi-label (MIML) learning, datasets are given in the form of bags, each of which contains multiple instances and is associated with multiple labels. This paper considers a novel instance clustering problem in MIML learning, where the bag labels are used as background knowledge to help group instances into clusters. The goal is to recover the class labels or to find the subcl...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2012
ISSN: 0004-3702
DOI: 10.1016/j.artint.2011.10.002