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The classification of images is an important topic in several image-concerned areas. A newly proposed concept in the literature of machine learning, the Universum sample, defined as the sample that doesn't belong to any of the task-concerned classes, has been proved to be useful in training the classifiers. However, not all the Universum samples are equally helpful according to our research. In this...
We propose a new dimensionality reduction method called maximum margin embedding (MME), which targets to projecting data samples into the most discriminative subspace, where clusters are most well-separated. Specifically, MME projects input patterns onto the normal of the maximum margin separating hyperplanes. As a result, MME only depends on the geometry of the optimal decision boundary and not on...
In this paper, we propose two general multiple instance active learning (MIAL) algorithms, multiple-instance active learning with a simple margin strategy (S-MIAL) and multiple- instance active learning with fisher information (F-MIAL), and apply them to the relevance feedback in localized content based image retrieval (LCBIR). S-MIAL considers the most ambiguous picture as the most valuable one,...
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