Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
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Rong Jin (Michigan State University), Shijun Wang...
Boosted one-versus-all (OVA) classifiers are commonly used in multiclass problems, such as generic object recognition, biometrics-based identification, or gesture recognition. Join...
Alexandra Stefan (University of Texas at Arlington...
Many machine learning algorithms require the summation of Gaussian kernel
functions, an expensive operation if implemented straightforwardly. Several methods
have been proposed t...
Vlad I. Morariu1, Balaji V. Srinivasan, Vikas C. R...
Object/scene detection by discriminative kernel-based classification has gained great interest due to its promising performance and flexibility. In this paper, unlike traditional ...
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...