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This paper explores the idea of integrating powerful optimization and planning techniques with knowledge representation formalisms of the Semantic Web. The paper outlines a language paradigm that combines algebraic and logical modelling of constraints and objective functions. It provides a method for instantiation of optimization and planning problems with Semantic Web background knowledge, by modelling...
In this paper, we study the problem of finding teams of experts from an expert network while optimizing three objectives. Given a project, the objective is to find teams of experts that cover all the required skills and also optimize the communication cost as well as the personnel cost and the expertise level of the team members. The expert network is modeled as a graph, where nodes represent experts...
Fuzzy clustering which can implement flexible classification is very useful but sometimes calculates the degrees of belongingness of an objects to a cluster too exactly. To solve this problem, a new clustering method called rough k-means (RKM) is proposed by Lingras et al. RKM which is an extended method by using rough set representation can classify more roughly than fuzzy clustering without lack...
This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the...
We present a new decomposition algorithm for training bound-constrained Support Vector Machines in this paper. When selecting indices into the working set, only first order derivative information of the objective function in the optimization model is required. Therefore, the resulting working set selection strategy is simple and can be implemented easily. The new algorithm is proved to be global convergent...
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