Xishuang Han

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20 of J unger and Mutzel 17, 16]. The exception was on the tg test problem class, on which the solutions found by GRASP were worse than those of the branch-and-cut method. The experiments indicate that approximate solutions of the two-coloring maximum independent set problems, obtained with the GRASP for maximumindependent set of 9], is not suucient to(More)
Object detection from images is generally achieved through a supervised learning manner. However, in many real applications, to provide instance level label is still costly. Thus, weakly supervised approach is proposed and naturally cast as a Multiple Instance Learning (MIL) problem. Traditional MIL methods typically learn discriminative classifiers from(More)
The work presented in this paper innovates the business process of construction industry by analyzing the value chain throughout the construction project lifecycle and then proposing a new service-oriented value chain management system. In the new value chain management system, this work applies the business process reengineering (BPR) approach for(More)
In this paper, we address the problem of learning object class models from weakly labeled training images, where labels of object classes are only provided at image level. Such weakly supervised object learning can be considered as a Multiple Instance Learning (MIL) problem. We observed that object instances of a common category are visually similar and(More)
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