We propose density-ratio bagging (dragging), a semi-supervised extension of bootstrap aggregation (bagging) method. Additional unlabeled training data are used to calculate the weight on each labeled training point by a density-ratio estimator. The weight is then used to construct a weighted labeled empirical distribution, from which bags of bootstrap samples are drawn. Asymptotically, dragging...

The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on developing Density-ratio (DR) estimation techniques ...

Floor-planning is a fundamental step in VLSI chip design. Based upon the concept of orderly spanning trees, we present a simple O(n)-time algorithm to construct a floorplan for any n-node plane triangulation. In comparison with previous floor-planning algorithms in the literature, our solution is not only simpler in the algorithm itself, but also produces floor-plans which require fewer module ...

Impulsive loads and walking loads can generate problematic structural vibrations in flooring-systems. Measures that may be taken to mitigate the problem would often be to consider the implementation of a tuned mass damper or even more advanced vibration control technologies; this in order to add damping to the structure. Basically also passive humans on a floor act as a damping source, but it a...

In the current scenario, interference is one of the most challenging problems we are facing in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduct...