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Aggregate outputs learning is a newly proposed setting in data mining and machine learning. It differs from the classical supervised learning setting in that, training samples are packed into bags with only the aggregate outputs (labels for classification or real values for regression) provided. This problem is associated with several kinds of application background. We focus on the aggregate outputs...
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,...
In this paper, we propose an information fusion framework for the semi-supervised distance-based music genre classification problem. We make use of the regularized least-square framework as the basic classifier, which only involves the similarity scores among different music tracks. We present a similarity score that multiplies different scores based on different distance measures. Particularly the...
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