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Content-based image auto-annotation becomes a hot research topic owing to the development of image retrieval system and the storing technology of multimedia information. It is a key step in most of those image processing applications. In this work, we adopt active learning to image annotation for reducing the number of labeled images required for supervised learning procedure. Localized Generalization...
Owing to the growth of Internet and computer technology, pattern recognition for large-scale datasets has become one of the hot research topics. The major challenges are to reduce the human efforts involved and to improve the efficiency. Traditional passive learning methods require labeling of all training samples may not be feasible in large-scale recognition problems because of the requirement of...
The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving...
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