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A Semi-supervised Segmentation Fusion algorithm is proposed using consensus and distributed learning. The aim of Unsupervised Segmentation Fusion (USF) is to achieve a consensus among different segmentation outputs obtained from different segmentation algorithms by computing an approximate solution to the NP problem with less computational complexity. Semi-supervision is incorporated in USF using...
Adaptive data-driven dictionaries for sparse approximations provide superior performance compared to predefined dictionaries in applications involving representation and classification of data. In this paper, we propose a novel algorithm for learning global dictionaries particularly suited to the sparse representation of natural images. The proposed algorithm uses a hierarchical energy based learning...
In order to overcome the shortcoming of nearest neighbor-clustering algorithm in the cluster center determined, the cluster width of the acquisition, and the hidden nodes learning. A FCM strategy is being proposed to determine the cluster center, introducing the target function and the LMS method to make the cluster width adjusted adaptively, and a pruning strategy is adopted to cut the redundant...
To improve the training speed of SVM, we propose a new SVM training approach which takes thick convex-hull as training set. The approach makes better use of the margin information for classification of data sets, and thus extends the use of convex hull to approximately linearly separable problems. Experiments on 5 UCI data sets indicate that the approach speeds up training of SVM with guarantee of...
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