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Found 3 papers, 1 papers with code
An information-theoretic learning model based on importance sampling
A crucial assumption underlying the most current theory of machine learning is that the training distribution is identical to the test distribution.
Information Theoretical Importance Sampling Clustering
In this paper, we propose an information theoretical importance sampling based approach for clustering problems (ITISC) which minimizes the worst case of expected distortions under the constraint of distribution deviation.
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects.
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