Retrieving the Missing Data From Different Incomplete Soft Sets

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Data Filling Approach of Soft Sets under Incomplete Information

Incomplete information in a soft set restricts the usage of the soft set. To make the incomplete soft set more useful, in this paper, we propose a data filling approach for incomplete soft set in which missing data is filled in terms of the association degree between the parameters when stronger association exists between the parameters or in terms of the probability of objects appearing in the...

A Method for Retrieving Images from Noisy Incomplete Data

We propose here a nev method for retrieving an image from noisy, incomplete data. It is based on an idea incorporating the principle of maximum entropy, a veil known powerful method for image processing. An image is built on the computer by allocating grains to pixels one at a time on the basis of certain deterministic rules as to where each successive grain is placed in image space. The perfor...

Learning from Data Sets with Missing Labels

This paper consider the task of learning discriminative classifiers of data when some class labels are missing from the data set(so-called “semi-supervised” learning), specifically when the labeled data are not drawn from the same distribution as the unlabeled data. This is an important issue in domains in which learning from only the labeled samples can result in a classifier that is not appro...