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Bayesian Optimization or Efficient Global Optimization (EGO) is a global search strategy that is designed for expensive black-box functions. In this algorithm, a statistical model (usually the Gaussian process model) is constructed on some initial data samples. The global optimum is approached by iteratively maximizing a so-called acquisition function, that balances the exploration and exploitation...
In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental...
Real-world datasets often have representations in multiple views or come from multiple sources. Exploiting consistent or complementary information from multi-view data, multi-view clustering aims to get better clustering quality rather than relying on the individual view. In this paper, we propose a novel multi-view clustering method called multi-view concept clustering based on concept factorization...
The problem of network disintegration has broad applications and recently has received growing attention, such as network confrontation and disintegration of harmful networks. This paper presents an optimized disintegration strategy model for complex networks and introduces the GA optimization method into the network disintegration problem to identify the optimal disintegration strategy, which is...
Kriging-based Global optimization has been proposed and extensively used for solving black-box optimization problems with expensive function evaluations. The performance of such algorithm relies heavily on the effectiveness of the infill criterion that is used to decide which point to evaluate next. Two common infill criteria are, the probability of improvement (PI) and the expected improvement (EI)...
We propose a residual-consensus driven linear matching algorithm for simultaneous geometric parameter and point correspondence estimation. Using the linearization technique, we quantize geometric transformation into discrete levels with regard to each correspondence matrix. We identify the uncontaminated models by evaluating the statistical coherent of residual ordering and Maximum mean discrepancy...
This paper develops an opposition-based learning harmony search algorithm with mutation (OLHS-M) for solving global continuous optimization problems. The proposed method is different from the original harmony search (HS) in three aspects. Firstly, opposition-based learning technique is incorporated to the process of improvisation to enlarge the algorithm search space. Then, a new modified mutation...
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