ABSTRACT: Aiming at the problems of intensity inhomogeneity, boundary blurring and noise interference in the segmentation of three-dimensional volume data (such as medical images and industrial CT ...
The hype we have been sold for the past few years has been overwhelming. Hype Correction is the antidote. Can I ask you a question: How do you feel about AI right now? Are you still excited? When you ...
Add a description, image, and links to the expectation-maximization-algorithm topic page so that developers can more easily learn about it.
Descriptive set theorists study the niche mathematics of infinity. Now, they’ve shown that their problems can be rewritten in the concrete language of algorithms. All of modern mathematics is built on ...
Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States ...
ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
Submodular maximization is a significant area of interest in combinatorial optimization, with numerous real-world applications. A research team led by Xiaoming SUN from the State Key Lab of Processors ...
In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results