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Bayesian Inference with Posterior Regularization and Applications to Infinite Latent SVMs

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Figure

Figure 1: Illustration for the (a) hard and (b) soft constraints in the simple setting whichhas only three possible models
Figure 2: Graphical structures of (a) infinite latent SVM (iLSVM); and (b) multi-task in-finite latent SVM (MT-iLSVM)
Table 1: Classification accuracy and F1 scores on the TRECVID2003 and Flickr imagedata sets (Note: MMH and EFH have zero std because of their deterministicinitialization).
Figure 4: (Up) the overall average values of the latent features with standard deviationover different classes; and (Bottom) the per-class average values of latent featureslearned by iLSVM on the TRECVID data set.
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