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A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Inferring Label Sampling Mechanisms Semi-Supervised Learning
2015/8/21
We consider the situation in semi-supervised learning, where the “label sampling” mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of...
We propose a sketch-based sampling algorithm, which effectively exploits the data sparsity. Sampling methods have become popular in large-scale data mining and information retrieval, where high data s...
One Sketch For All:Theory and Application of Conditional Random Sampling
One Sketch For All Theory and Application Conditional Random Sampling
2015/8/21
Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise (l2, l1) distances, in static, large-scale, and sparse data. This study modifies the original CRS and exten...
Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss
Sequential estimation Point estimator Inverse binomial sampling
2010/12/3
Sequential estimation of the success probability p in inverse binomial sam-pling is considered in this paper. For any estimator ˆp, its quality is measured by the risk associated with normalized ...
Path sampling for particle filters with application to multi-target tracking
Path sampling particle filters with application multi-target tracking
2010/12/3
In recent work [15], we have presented a novel approach for improving particle filters for multi-target tracking. The suggested approach was based on Girsanov’s change of measure theorem for stochasti...
Sampling and Recovery of Multidimensional Bandlimited Functions via Frames
Sampling and Recovery Multidimensional Bandlimited Functions
2010/12/3
In this paper, we investigate frames for L2[−, ]d consisting of exponential functions in connection to oversampling and nonuniform sampling of bandlimited func-tions.