Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
نویسندگان
چکیده
منابع مشابه
Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost wh...
متن کاملMulti-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...
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Xia Sun 1,*, Jingting Xu 1, Changmeng Jiang 1, Jun Feng 1, Su-Shing Chen 2 and Feijuan He 3 1 School of Information Science and Technology, Northwest University, Xi’an 710069, China; [email protected] (J.X.); [email protected] (C.J.); [email protected] (J.F.) 2 Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA; [email protected] 3 Department o...
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...
متن کاملMulti-Label Learning by Instance Differentiation
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is explicitly expressed in the output space by being associated with multiple class labels. While on the other hand, its ambiguity is only implicitly encoded in the input space by being represented by only a single insta...
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ژورنال
عنوان ژورنال: Applied Sciences
سال: 2016
ISSN: 2076-3417
DOI: 10.3390/app6060160