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Collecting data is very easy now owing to fast computers and ease of Internet access. It raises the problem of the curse of dimensionality to supervised classification problems. In our previous work, an Intra-Prototype / Inter-Class Separability Ratio (IPICSR) model is proposed to select relevant features for semi-supervised classification problems. In this work, a new margin based feature selection...
A new dynamic classifier fusion method named L-GEM fusion method (LFM) for multiple classifier systems (MCSs) is proposed. The localized generalization error upper bound for the neighborhood of a testing sample is calculated and used to estimate the local competence of base classifiers in MCSs. Different from the recent dynamic classifier selection methods, the proposed method consider not only the...
Relevance feedback has been developed for several years and becomes an effective method for capturing userpsilas concepts to improve the performance of content-based image retrieval (CBIR). In contrast to fully labeled training dataset in supervised learning, semi-supervised learning and active learning deal with training dataset with only a small portion of labeled samples. This is more realistic...
In this work, the localized generalization error model (L-GEM) for multilayer perceptron neural network (MLPNN) is derived. The L-GEM is inspired by the fact that a classifier should not be required to recognize unseen samples that are very different from the training samples. Therefore, evaluating a classifier by very different unseen samples may be counter-productive. In the L-GEM, the ldquolocalrdquo...
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